{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "tBD6AYJ6hzX3", "outputId": "33b6eba3-1188-4ad4-826e-44d010b59590" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: fastapi in ./lib/python3.11/site-packages (0.115.12)\n", "Requirement already satisfied: uvicorn in ./lib/python3.11/site-packages (0.34.2)\n", "Requirement already satisfied: unsloth in ./lib/python3.11/site-packages (2025.5.1)\n", "Requirement already satisfied: sentence-transformers in ./lib/python3.11/site-packages (3.4.1)\n", "Requirement already satisfied: joblib in ./lib/python3.11/site-packages (1.4.2)\n", "Requirement already satisfied: torch in ./lib/python3.11/site-packages (2.5.1)\n", "Requirement already satisfied: spacy in ./lib/python3.11/site-packages (3.8.5)\n", "Requirement already satisfied: transformers in ./lib/python3.11/site-packages (4.51.3)\n", 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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Requirement already satisfied: huggingface_hub[hf_xet] in ./lib/python3.11/site-packages (0.30.2)\n", "Requirement already satisfied: filelock in ./lib/python3.11/site-packages (from huggingface_hub[hf_xet]) (3.17.0)\n", "Requirement already satisfied: fsspec>=2023.5.0 in ./lib/python3.11/site-packages (from huggingface_hub[hf_xet]) (2024.9.0)\n", "Requirement already satisfied: packaging>=20.9 in ./lib/python3.11/site-packages (from huggingface_hub[hf_xet]) (24.2)\n", "Requirement already satisfied: pyyaml>=5.1 in ./lib/python3.11/site-packages (from huggingface_hub[hf_xet]) (6.0.2)\n", "Requirement already satisfied: requests in ./lib/python3.11/site-packages (from huggingface_hub[hf_xet]) (2.32.3)\n", "Requirement already satisfied: tqdm>=4.42.1 in ./lib/python3.11/site-packages (from huggingface_hub[hf_xet]) (4.67.1)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in ./lib/python3.11/site-packages (from huggingface_hub[hf_xet]) (4.12.2)\n", "Requirement already satisfied: hf-xet>=0.1.4 in ./lib/python3.11/site-packages (from huggingface_hub[hf_xet]) (1.1.0)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in ./lib/python3.11/site-packages (from requests->huggingface_hub[hf_xet]) (3.4.1)\n", "Requirement already satisfied: idna<4,>=2.5 in ./lib/python3.11/site-packages (from requests->huggingface_hub[hf_xet]) (3.10)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in ./lib/python3.11/site-packages (from requests->huggingface_hub[hf_xet]) (2.3.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in ./lib/python3.11/site-packages (from requests->huggingface_hub[hf_xet]) (2024.12.14)\n", "\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "/home/citnlp/yechiel/bin/python: No module named spacy\n" ] } ], "source": [ "# Google Colab cell for cloud API\n", "\n", "# Install dependencies\n", "!pip install fastapi uvicorn unsloth sentence-transformers joblib torch spacy transformers huggingface-hub pydantic numpy pyngrok\n", "! pip install huggingface_hub[hf_xet]\n", "\n", "# Install spaCy model\n", "!python -m spacy download en_core_web_sm\n", "\n", "# Install ngrok\n", "# !pip install pyngrok\n", "\n", "# ! curl -X POST https://7ad2-35-197-106-150.ngrok-free.app/generate_sentences \\\n", "# -H \"Content-Type: application/json\" \\\n", "# -d '{\"level\": \"A1\", \"num_sentences\": 2, \"user_id\": 1}'\n", "\n", "# curl -X POST https://b84d-34-124-230-90.ngrok-free.app/generate_sentences \\\n", "# -H \"Content-Type: application/json\" \\\n", "# -d '{\"level\": \"A1\", \"num_sentences\": 2, \"user_id\": 1, \"topic\": \"food\"}'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "πŸ¦₯ Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "Unsloth: Failed to patch Gemma3ForConditionalGeneration.\n", "πŸ¦₯ Unsloth Zoo will now patch everything to make training faster!\n", "Loading all models...\n", "==((====))== Unsloth 2025.5.1: Fast Llama patching. Transformers: 4.51.3.\n", " \\\\ /| NVIDIA GeForce RTX 4090. Num GPUs = 1. Max memory: 23.988 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.5.1+cu124. CUDA: 8.9. CUDA Toolkit: 12.4. Triton: 3.1.0\n", "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = False]\n", " \"-____-\" Free license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Unsloth 2025.5.1 patched 32 layers with 0 QKV layers, 0 O layers and 0 MLP layers.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Finetuned generator model 'Mr-FineTuner/With_synthetic_Dataset_llama-1epoch' loaded successfully!\n", "Loading plain distractor model...\n", "==((====))== Unsloth 2025.5.1: Fast Llama patching. Transformers: 4.51.3.\n", " \\\\ /| NVIDIA GeForce RTX 4090. Num GPUs = 1. Max memory: 23.988 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.5.1+cu124. CUDA: 8.9. CUDA Toolkit: 12.4. Triton: 3.1.0\n", "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = False]\n", " \"-____-\" Free license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n", "Plain distractor model 'unsloth/llama-3-8b-Instruct-bnb-4bit' loaded successfully!\n", "Loading validator model...\n", "Validator models loaded successfully!\n", "Loading spaCy model...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO: Started server process [9399]\n", "INFO: Waiting for application startup.\n", "INFO: Application startup complete.\n", "INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "spaCy model loaded successfully!\n", "Starting server on port 8000...\n", "Ngrok tunnel created: https://ccbf-103-119-147-234.ngrok-free.app\n", "\n", "API is available at: https://ccbf-103-119-147-234.ngrok-free.app/generate_sentences\n" ] } ], "source": [ "from fastapi import FastAPI, HTTPException, Request\n", "from pydantic import BaseModel\n", "import uvicorn\n", "from unsloth import FastLanguageModel\n", "from sentence_transformers import SentenceTransformer\n", "import joblib\n", "import numpy as np\n", "import re\n", "import random\n", "from huggingface_hub import snapshot_download\n", "import torch\n", "import spacy\n", "from pyngrok import ngrok\n", "import nest_asyncio\n", "import threading\n", "import asyncio\n", "import time\n", "from typing import List, Dict, Optional\n", "from transformers import StoppingCriteria\n", "import datetime\n", "import os\n", "\n", "# Allow nested event loops for Colab\n", "nest_asyncio.apply()\n", "\n", "# Define FastAPI app\n", "app = FastAPI()\n", "\n", "# Global variables for loaded models and constants\n", "GENERATOR_MODEL = None\n", "GENERATOR_TOKENIZER = None\n", "DISTRACTOR_MODEL = None\n", "DISTRACTOR_TOKENIZER = None\n", "EVALUATOR_MODEL = None\n", "EMBEDDER_MODEL = None\n", "SPACY_MODEL = None\n", "MAX_SEQ_LENGTH = 2048\n", "\n", "# Define request models\n", "class SentenceRequest(BaseModel):\n", " level: str\n", " num_sentences: int = 1\n", " user_id: int\n", " topic: Optional[str] = None\n", " question_types: Optional[List[str]] = [\"selection\", \"labeling\", \"fill_in_blank\", \"multiple_choice\", \"arrange\"]\n", "\n", "class SelectionRequest(BaseModel):\n", " selected_sentence: str\n", " sentences: str # Changed to str to match the | delimiter\n", " correct_sentence: str\n", " target_level: str\n", " user_id: int\n", "\n", "class LabelingRequest(BaseModel):\n", " sentence: str\n", " user_labels: Dict[str, str]\n", " target_level: str\n", " user_id: int\n", "\n", "class FillInBlankRequest(BaseModel):\n", " sentence: str\n", " user_answer: str\n", " correct_answer: str\n", " target_level: str\n", " user_id: int\n", "\n", "class MultipleChoiceRequest(BaseModel):\n", " selected_option: str\n", " options: List[str]\n", " correct_answer: str\n", " target_level: str\n", " user_id: int\n", "\n", "class ArrangeRequest(BaseModel):\n", " sentence: str\n", " user_arrangement: List[str]\n", " correct_sentence: str\n", " target_level: str\n", " user_id: int\n", "\n", "# Define response models\n", "class SentenceResponse(BaseModel):\n", " success: bool\n", " sentences: List[Dict]\n", " message: str\n", " processing_time: float\n", " timestamp: str\n", "\n", "class SelectionResponse(BaseModel):\n", " success: bool\n", " is_correct: bool\n", " score: float\n", " message: str\n", " processing_time: float\n", " timestamp: str\n", "\n", "class LabelingResponse(BaseModel):\n", " success: bool\n", " is_correct: bool\n", " score: float\n", " message: str\n", " processing_time: float\n", " timestamp: str\n", "\n", "class FillInBlankResponse(BaseModel):\n", " success: bool\n", " is_correct: bool\n", " score: float\n", " message: str\n", " processing_time: float\n", " timestamp: str\n", "\n", "class MultipleChoiceResponse(BaseModel):\n", " success: bool\n", " is_correct: bool\n", " score: float\n", " message: str\n", " processing_time: float\n", " timestamp: str\n", "\n", "class ArrangeResponse(BaseModel):\n", " success: bool\n", " is_correct: bool\n", " score: float\n", " message: str\n", " processing_time: float\n", " timestamp: str\n", "\n", "class StopOnTokens(StoppingCriteria):\n", " def __init__(self, tokenizer, stop_strings):\n", " self.tokenizer = tokenizer\n", " self.stop_strings = stop_strings\n", " self.stop_ids = [tokenizer.encode(s, add_special_tokens=False) for s in stop_strings]\n", "\n", " def __call__(self, input_ids, scores, **kwargs):\n", " for stop_id in self.stop_ids:\n", " if len(input_ids[0]) >= len(stop_id):\n", " if all(input_ids[0][-len(stop_id):] == stop_id):\n", " return True\n", " return False\n", "\n", "# Model loading function\n", "def load_models():\n", " global GENERATOR_MODEL, GENERATOR_TOKENIZER, DISTRACTOR_MODEL, DISTRACTOR_TOKENIZER, EVALUATOR_MODEL, EMBEDDER_MODEL, SPACY_MODEL\n", " print(\"Loading all models...\")\n", " dtype = None\n", " load_in_4bit = True\n", " # generator_model_name = \"Mr-FineTuner/Test_02_llama_1epoch_trainPercen_myValidator_fix\"\n", " generator_model_name = \"Mr-FineTuner/With_synthetic_Dataset_llama-1epoch\"\n", " try:\n", " GENERATOR_MODEL, GENERATOR_TOKENIZER = FastLanguageModel.from_pretrained(\n", " model_name=generator_model_name,\n", " max_seq_length=MAX_SEQ_LENGTH,\n", " dtype=dtype,\n", " load_in_4bit=load_in_4bit,\n", " )\n", " if GENERATOR_TOKENIZER.pad_token is None:\n", " GENERATOR_TOKENIZER.pad_token = GENERATOR_TOKENIZER.eos_token\n", " GENERATOR_MODEL = FastLanguageModel.for_inference(GENERATOR_MODEL)\n", " print(f\"Finetuned generator model '{generator_model_name}' loaded successfully!\")\n", " except Exception as e:\n", " print(f\"Error loading finetuned generator model: {str(e)}\")\n", " raise\n", " print(\"Loading plain distractor model...\")\n", " distractor_model_name = \"unsloth/llama-3-8b-Instruct-bnb-4bit\"\n", " try:\n", " DISTRACTOR_MODEL, DISTRACTOR_TOKENIZER = FastLanguageModel.from_pretrained(\n", " model_name=distractor_model_name,\n", " max_seq_length=MAX_SEQ_LENGTH,\n", " dtype=dtype,\n", " load_in_4bit=load_in_4bit,\n", " )\n", " if DISTRACTOR_TOKENIZER.pad_token is None:\n", " DISTRACTOR_TOKENIZER.pad_token = DISTRACTOR_TOKENIZER.eos_token\n", " DISTRACTOR_MODEL = FastLanguageModel.for_inference(DISTRACTOR_MODEL)\n", " print(f\"Plain distractor model '{distractor_model_name}' loaded successfully!\")\n", " except Exception as e:\n", " print(f\"Error loading plain distractor model: {str(e)}\")\n", " raise\n", " print(\"Loading validator model...\")\n", " try:\n", " eval_model_path = snapshot_download(\n", " repo_id=\"Mr-FineTuner/Skripsi_validator_best_model\",\n", " allow_patterns=[\"model_mlp.joblib\"],\n", " local_dir=\"validator_model\"\n", " )\n", " classifier_path = os.path.join(eval_model_path, \"model_mlp.joblib\")\n", " if not os.path.exists(classifier_path):\n", " raise FileNotFoundError(f\"model_mlp.joblib not found in {eval_model_path}\")\n", " EVALUATOR_MODEL = joblib.load(classifier_path)\n", " EMBEDDER_MODEL = SentenceTransformer('BAAI/bge-base-en-v1.5')\n", " print(\"Validator models loaded successfully!\")\n", " except Exception as e:\n", " print(f\"Error loading validator models: {str(e)}\")\n", " raise\n", " print(\"Loading spaCy model...\")\n", " try:\n", " SPACY_MODEL = spacy.load(\"en_core_web_sm\")\n", " print(\"spaCy model loaded successfully!\")\n", " except Exception as e:\n", " print(f\"Error loading spaCy model: {str(e)}\")\n", " raise\n", "\n", "# Sentence generation logic\n", "def generate_sentences_internal(level: str, num_sentences: int, user_id: int, topic: Optional[str] = None, question_types: List[str] = [\"selection\", \"labeling\", \"fill_in_blank\", \"multiple_choice\", \"arrange\"]) -> List[Dict]:\n", " global GENERATOR_MODEL, GENERATOR_TOKENIZER, DISTRACTOR_MODEL, DISTRACTOR_TOKENIZER, EVALUATOR_MODEL, EMBEDDER_MODEL, SPACY_MODEL\n", " if None in [GENERATOR_MODEL, GENERATOR_TOKENIZER, DISTRACTOR_MODEL, DISTRACTOR_TOKENIZER, EVALUATOR_MODEL, EMBEDDER_MODEL, SPACY_MODEL]:\n", " raise ValueError(\"One or more models failed to load\")\n", "\n", " # Contraction mapping for expansion\n", " CONTRACTION_MAP = {\n", " \"'ve\": \"have\",\n", " \"'s\": \"is\",\n", " \"'re\": \"are\",\n", " \"'m\": \"am\",\n", " \"'ll\": \"will\",\n", " \"'d\": \"had\"\n", " }\n", "\n", " error_counts = {\n", " \"duplicate_sentence\": 0,\n", " \"cefr_mismatch\": 0,\n", " \"no_valid_verb\": 0,\n", " \"no_valid_constituents\": 0,\n", " \"insufficient_constituents\": 0,\n", " \"multiple_clauses\": 0,\n", " \"passive_voice\": 0,\n", " \"output_pattern_removed\": 0,\n", " \"duplicate_selection\": 0,\n", " \"invalid_incorrect_sentence\": 0,\n", " \"invalid_fill_in_blank\": 0,\n", " \"invalid_multiple_choice\": 0,\n", " \"invalid_arrange\": 0,\n", " \"no_full_stop\": 0\n", " }\n", "\n", " def predict_sentiment(text: str) -> int:\n", " embedding = EMBEDDER_MODEL.encode([text])\n", " probs = EVALUATOR_MODEL.predict_proba(embedding)[0]\n", " prediction = np.argmax(probs) + 1\n", " return max(1, min(6, int(prediction)))\n", "\n", " cefr_to_score = {'A1': 1, 'A2': 2, 'B1': 3, 'B2': 4, 'C1': 5, 'C2': 6}\n", " score_to_cefr = {1: 'A1', 2: 'A2', 3: 'B1', 4: 'B2', 5: 'C1', 6: 'C2'}\n", "\n", " def clean_sentence(text: str) -> Optional[str]:\n", " cleaned = re.sub(r'\\n+#+\\s*Output\\s*:', '', text, flags=re.IGNORECASE)\n", " cleaned = re.sub(r'\\n+\\s*###\\s*Output\\s*:', '', cleaned, flags=re.IGNORECASE)\n", " cleaned = cleaned.strip()\n", " match = re.match(r'^(.*?)\\.', cleaned)\n", " if match:\n", " cleaned = match.group(1) + '.'\n", " else:\n", " print(\"Debug: No full-stop found in sentence, rejecting.\")\n", " return None\n", " cleaned = cleaned.strip()\n", " if not cleaned:\n", " print(\"Debug: Cleaned sentence is empty, rejecting.\")\n", " return None\n", " return cleaned\n", "\n", " def is_article_correct(sent: str, orig_doc) -> bool:\n", " doc = SPACY_MODEL(sent)\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"nsubj\" and i > 0:\n", " prev = doc[i-1].text.lower()\n", " if prev in [\"a\", \"an\"]:\n", " first_letter = orig_doc[i].text[0].lower()\n", " is_vowel = first_letter in 'aeiou'\n", " if (prev == \"a\" and is_vowel) or (prev == \"an\" and not is_vowel):\n", " return False\n", " elif prev == \"the\":\n", " return False\n", " elif token.dep_ == \"nsubj\" and i == 0:\n", " if token.tag_ in [\"NN\", \"NNP\"]:\n", " return False\n", " return True\n", "\n", " def is_subject_verb_agreement_correct(sent: str) -> bool:\n", " doc = SPACY_MODEL(sent)\n", " subject = None\n", " verb = None\n", " for token in doc:\n", " if token.dep_ == \"nsubj\":\n", " subject = token\n", " if token.dep_ == \"ROOT\" and token.pos_ in [\"VERB\", \"AUX\"]:\n", " verb = token\n", " if subject and verb:\n", " is_subject_plural = subject.tag_ in [\"NNS\", \"NNPS\"]\n", " is_verb_singular = verb.text.lower() in [\"is\", \"was\", \"has\"]\n", " return not (is_subject_plural != is_verb_singular)\n", " return True\n", "\n", " def create_selection_question(sentence: str, level: str) -> Dict:\n", " error_types = {\n", " 'A1': [\"spelling\", \"article\", \"number_agreement\"],\n", " 'A2': [\"spelling\", \"article\", \"tense\"],\n", " 'B1': [\"tense\", \"preposition\", \"word_choice\"],\n", " 'B2': [\"tense\", \"preposition\", \"agreement\"],\n", " 'C1': [\"complex_tense\", \"idiom\", \"word_form\"],\n", " 'C2': [\"complex_tense\", \"idiom\", \"subjunctive\"]\n", " }\n", " available_errors = error_types.get(level, error_types['B1'])\n", " if len(available_errors) < 2:\n", " available_errors = available_errors + [\"spelling\"]\n", " errors = random.sample(available_errors, 2)\n", "\n", " doc = SPACY_MODEL(sentence)\n", " words = [token.text for token in doc]\n", "\n", " def introduce_error(sent: str, error_type: str) -> str:\n", " doc = SPACY_MODEL(sent)\n", " words = [token.text for token in doc]\n", " modified = sent\n", " if error_type == \"spelling\":\n", " for i, word in enumerate(words):\n", " if len(word) > 3 and word.isalpha():\n", " if len(word) > 4:\n", " words[i] = word[:-2] + random.choice('ae') + random.choice('mn')\n", " else:\n", " words[i] = word[:-1] + random.choice('ae')\n", " modified = \" \".join(words).strip()\n", " if SPACY_MODEL(words[i])[0].is_oov:\n", " break\n", " else:\n", " modified = sent\n", " elif error_type == \"article\":\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"nsubj\" and i > 0 and words[i-1].lower() in [\"a\", \"an\"]:\n", " words[i-1] = \"\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif token.dep_ == \"nsubj\":\n", " first_letter = token.text[0].lower()\n", " is_vowel = first_letter in 'aeiou'\n", " words[i] = f\"{'a' if is_vowel else 'an'} {words[i]}\"\n", " modified = \" \".join(words).strip()\n", " break\n", " else:\n", " modified = sent\n", " elif error_type == \"number_agreement\":\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"nsubj\":\n", " is_plural = token.tag_ in [\"NNS\", \"NNPS\"]\n", " if is_plural:\n", " words[i] = SPACY_MODEL(token.lemma_)[0].text\n", " else:\n", " words[i] = token.text + \"s\"\n", " for j, t in enumerate(doc):\n", " if t.dep_ == \"ROOT\" and t.text.lower() in [\"is\", \"are\"]:\n", " words[j] = \"are\" if t.text.lower() == \"is\" else \"is\"\n", " break\n", " modified = \" \".join(words).strip()\n", " break\n", " else:\n", " modified = sent\n", " elif error_type == \"tense\":\n", " for i, token in enumerate(doc):\n", " if token.pos_ == \"VERB\" and token.text.lower() not in [\"be\", \"can\"]:\n", " words[i] = SPACY_MODEL(token.lemma_)[0].text + \"ed\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"preposition\":\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"prep\":\n", " words[i] = \"to\" if token.text != \"to\" else \"in\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"word_choice\":\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"dobj\":\n", " words[i] = \"banana\" if token.text.lower() != \"banana\" else \"orange\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"agreement\":\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"nsubj\" and token.text.lower() == \"i\":\n", " words[i] = \"We\"\n", " for v in doc:\n", " if v.pos_ == \"VERB\" and v.text.lower() in [\"like\", \"eat\"]:\n", " words[v.i] = v.text + \"s\"\n", " break\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"complex_tense\":\n", " for i, token in enumerate(doc):\n", " if token.pos_ == \"VERB\":\n", " words[i] = f\"had {token.lemma_}ed\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"idiom\":\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"dobj\":\n", " words[i] = \"moon\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"word_form\":\n", " for i, token in enumerate(doc):\n", " if token.pos_ == \"VERB\":\n", " words[i] = token.lemma_ + \"ing\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"subjunctive\":\n", " for i, token in enumerate(doc):\n", " if token.pos_ == \"VERB\":\n", " words[i] = token.lemma_\n", " modified = \" \".join(words).strip()\n", " break\n", " modified = clean_sentence(modified)\n", " if modified is None or modified.strip() == sent.strip():\n", " for i, word in enumerate(words):\n", " if len(word) > 3 and word.isalpha():\n", " words[i] = word[:-1] + random.choice('ae')\n", " modified = clean_sentence(\" \".join(words).strip())\n", " break\n", " return modified if modified else sent\n", "\n", " incorrect1 = introduce_error(sentence, errors[0])\n", " incorrect2 = introduce_error(sentence, errors[1])\n", " max_error_retries = 3\n", " error_retry_count = 0\n", " while (incorrect2 == incorrect1 or incorrect2 == sentence) and error_retry_count < max_error_retries:\n", " new_error = random.choice([e for e in available_errors if e != errors[0]])\n", " incorrect2 = introduce_error(sentence, new_error)\n", " errors[1] = new_error\n", " error_retry_count += 1\n", " if incorrect2 == incorrect1 or incorrect2 == sentence:\n", " for i, word in enumerate(words):\n", " if len(word) > 3 and word.isalpha():\n", " words[i] = word[:-1] + 'u'\n", " incorrect2 = clean_sentence(\" \".join(words).strip())\n", " errors[1] = \"spelling\"\n", " break\n", " sentences = [sentence, incorrect1, incorrect2]\n", " for i, s in enumerate(sentences):\n", " if s != sentence:\n", " is_valid = True\n", " try:\n", " if errors[i-1] == \"article\" and is_article_correct(s, doc):\n", " is_valid = False\n", " elif errors[i-1] == \"number_agreement\" and is_subject_verb_agreement_correct(s):\n", " is_valid = False\n", " elif errors[i-1] == \"spelling\":\n", " s_doc = SPACY_MODEL(s)\n", " for token, orig_token in zip(s_doc, doc):\n", " if token.text != orig_token.text and not token.is_oov:\n", " is_valid = False\n", " break\n", " except Exception as e:\n", " print(f\"Debug: Validation error for '{s}': {str(e)}\")\n", " is_valid = False\n", " if not is_valid:\n", " print(f\"Debug: Invalid incorrect sentence '{s}' for error '{errors[i-1]}'\")\n", " error_counts[\"invalid_incorrect_sentence\"] += 1\n", " new_error = random.choice([e for e in available_errors if e != errors[i-1]])\n", " sentences[i] = introduce_error(sentence, new_error)\n", " errors[i-1] = new_error\n", " if len(set(sentences)) != 3:\n", " print(f\"Debug: Duplicate sentences detected in selection question: {sentences}\")\n", " error_counts[\"duplicate_selection\"] += 1\n", " incorrect2 = introduce_error(sentence, random.choice(available_errors))\n", " sentences = [sentence, incorrect1, incorrect2]\n", " if len(set(sentences)) != 3:\n", " for i, word in enumerate(words):\n", " if len(word) > 3 and word.isalpha():\n", " words[i] = word[:-1] + 'u'\n", " incorrect2 = clean_sentence(\" \".join(words).strip())\n", " sentences = [sentence, incorrect1, incorrect2]\n", " break\n", " random.shuffle(sentences)\n", " return {\n", " \"question_text\": \"Choose the correct sentence.\",\n", " \"sentences\": \"|\".join(sentences), # Use | as delimiter\n", " \"correct_sentence\": sentence\n", " }\n", "\n", " def create_fill_in_blank(sentence: str, level: str) -> Optional[Dict]:\n", " doc = SPACY_MODEL(sentence)\n", " verb = None\n", " verb_index = None\n", " verb_lemma = None\n", " for i, token in enumerate(doc):\n", " if token.pos_ in [\"VERB\", \"AUX\"] and token.dep_ in [\"ROOT\", \"cop\"]:\n", " if token.text.lower() not in [\"to\"]:\n", " verb = token.text\n", " verb_index = i\n", " verb_lemma = token.lemma_.lower()\n", " break\n", " if not verb or not verb_lemma:\n", " print(\"Debug: No valid verb for fill-in-the-blank, skipping.\")\n", " error_counts[\"invalid_fill_in_blank\"] += 1\n", " return None\n", " words = [token.text for token in doc]\n", " words[verb_index] = \"__\"\n", " blanked_sentence = \" \".join(words).strip()\n", " question_text = f\"{blanked_sentence[:-1]} ({verb_lemma}).\"\n", " return {\n", " \"question_text\": question_text,\n", " \"correct_answer\": verb\n", " }\n", "\n", " def generate_contextual_distractors(target_verb: str, sentence: str, num_distractors: int = 3) -> List[str]:\n", " print(f\"Debug: Starting distractor generation for verb '{target_verb}' in sentence '{sentence}'\")\n", " target_doc = SPACY_MODEL(target_verb)\n", " target_tense = None\n", " for token in target_doc:\n", " if token.pos_ == \"VERB\":\n", " target_tense = token.tag_\n", " break\n", " print(f\"Debug: Target verb tense tag: {target_tense if target_tense else 'None'}\")\n", " tense_map = {\n", " 'VB': 'base form',\n", " 'VBD': 'past tense',\n", " 'VBG': 'gerund/present participle',\n", " 'VBN': 'past participle',\n", " 'VBP': 'present tense (non-3rd person singular)',\n", " 'VBZ': 'present tense (3rd person singular)'\n", " }\n", " tense_desc = tense_map.get(target_tense, 'base form') if target_tense else 'base form'\n", " print(f\"Debug: Tense description for prompt: {tense_desc}\")\n", " total_distractors_to_generate = 6\n", " prompt = (\n", " f\"Generate exactly {total_distractors_to_generate} English verbs that are distinct from '{target_verb}', \"\n", " f\"semantically related, and appropriate for CEFR level {level}. \"\n", " f\"The verbs must fit the context of the sentence '{sentence}' \"\n", " f\"and be in the {tense_desc} (e.g., '{target_verb}'). \"\n", " f\"Return only the verbs as a comma-separated list, with no additional text or non-verbs.\"\n", " )\n", " print(f\"Debug: LLM prompt:\\n{prompt}\")\n", " try:\n", " input_data = DISTRACTOR_TOKENIZER(\n", " prompt,\n", " return_tensors=\"pt\",\n", " padding=True,\n", " truncation=True,\n", " max_length=MAX_SEQ_LENGTH,\n", " ).to(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", " print(f\"Debug: Tokenized input data: {input_data}\")\n", " outputs = DISTRACTOR_MODEL.generate(\n", " input_ids=input_data[\"input_ids\"],\n", " attention_mask=input_data[\"attention_mask\"],\n", " max_new_tokens=50,\n", " pad_token_id=DISTRACTOR_TOKENIZER.pad_token_id,\n", " do_sample=True,\n", " temperature=0.6,\n", " top_k=50,\n", " top_p=0.85,\n", " )\n", " print(f\"Debug: Raw LLM output: {outputs}\")\n", " generated_text = DISTRACTOR_TOKENIZER.decode(outputs[0], skip_special_tokens=True)\n", " print(f\"Debug: Decoded LLM output: '{generated_text}'\")\n", " response_start = generated_text.find(\"<|start_header_id|>assistant<|end_header_id>\") + len(\"<|start_header_id|>assistant<|end_header_id>\")\n", " response_text = generated_text[response_start:].strip()\n", " print(f\"Debug: Extracted response text: '{response_text}'\")\n", " distractors = [v.strip() for v in response_text.split(\",\") if v.strip()]\n", " print(f\"Debug: Parsed distractors: {distractors}\")\n", " primary_distractors = []\n", " backup_distractors = []\n", " for verb in distractors:\n", " if not (len(verb.split()) == 1 and re.match(r'^[a-zA-Z]+$', verb)):\n", " print(f\"Debug: Invalid distractor '{verb}' - not a single alphabetic word\")\n", " continue\n", " if verb.lower() == target_verb.lower():\n", " print(f\"Debug: Invalid distractor '{verb}' - matches target verb\")\n", " continue\n", " doc = SPACY_MODEL(verb)\n", " is_verb = any(token.pos_ == \"VERB\" for token in doc)\n", " print(f\"Debug: Validating '{verb}' - is_verb: {is_verb}\")\n", " if not is_verb:\n", " print(f\"Debug: Invalid distractor '{verb}' - not recognized as a verb\")\n", " continue\n", " if len(primary_distractors) < num_distractors:\n", " primary_distractors.append(verb)\n", " elif len(backup_distractors) < num_distractors:\n", " backup_distractors.append(verb)\n", " if len(primary_distractors) >= num_distractors and len(backup_distractors) >= num_distractors:\n", " break\n", " print(f\"Debug: Primary distractors: {primary_distractors}\")\n", " print(f\"Debug: Backup distractors: {backup_distractors}\")\n", " selected_distractors = primary_distractors[:num_distractors]\n", " if len(selected_distractors) < num_distractors:\n", " print(f\"Debug: Insufficient primary distractors ({len(selected_distractors)}/{num_distractors}). Using backups.\")\n", " needed = num_distractors - len(selected_distractors)\n", " valid_backups = [v for v in backup_distractors if v not in selected_distractors]\n", " selected_distractors.extend(valid_backups[:needed])\n", " print(f\"Debug: Added backups: {valid_backups[:needed]}\")\n", " if len(selected_distractors) < num_distractors:\n", " print(f\"Debug: Insufficient distractors after backups ({len(selected_distractors)}/{num_distractors}). Using fallbacks.\")\n", " fallback_verbs = [\"walk\", \"talk\", \"look\", \"move\", \"say\"]\n", " fallback_verbs = [v for v in fallback_verbs if v.lower() != target_verb.lower() and v not in selected_distractors]\n", " selected_distractors.extend(fallback_verbs[:num_distractors - len(selected_distractors)])\n", " print(f\"Debug: Added fallbacks: {selected_distractors[len(selected_distractors) - (num_distractors - len(selected_distractors)):]}\")\n", " print(f\"Debug: Final distractors: {selected_distractors[:num_distractors]}\")\n", " return selected_distractors[:num_distractors]\n", " except Exception as e:\n", " print(f\"Debug: Error generating distractors with LLM: {str(e)}\")\n", " print(f\"Debug: Using fallback distractors due to error\")\n", " fallback_verbs = [\"walk\", \"talk\", \"look\"]\n", " return fallback_verbs[:num_distractors]\n", "\n", " def create_multiple_choice(sentence: str, level: str) -> Optional[Dict]:\n", " doc = SPACY_MODEL(sentence)\n", " verb = None\n", " verb_index = None\n", " for i, token in enumerate(doc):\n", " if token.pos_ in [\"VERB\", \"AUX\"] and token.dep_ in [\"ROOT\", \"cop\"]:\n", " if token.text.lower() not in [\"to\"]:\n", " verb = token.text\n", " verb_index = i\n", " break\n", " if not verb:\n", " print(\"Debug: No valid verb for multiple choice, skipping.\")\n", " error_counts[\"invalid_multiple_choice\"] += 1\n", " return None\n", " words = [token.text for token in doc]\n", " words[verb_index] = \"__\"\n", " blanked_sentence = \" \".join(words).strip()\n", " distractors = generate_contextual_distractors(verb, sentence)\n", " options = distractors + [verb]\n", " cleaned_options = []\n", " for opt in options:\n", " clean_opt = re.sub(r'[^a-zA-Z]', '', opt.lower()).strip()\n", " if clean_opt and len(clean_opt.split()) == 1:\n", " cleaned_options.append(clean_opt)\n", " if len(cleaned_options) < 4:\n", " print(\"Debug: Not enough valid options, returning None\")\n", " error_counts[\"invalid_multiple_choice\"] += 1\n", " return None\n", " random.shuffle(cleaned_options)\n", " return {\n", " \"question_text\": blanked_sentence,\n", " \"options\": cleaned_options,\n", " \"correct_answer\": verb.lower()\n", " }\n", "\n", " def create_arrange_question(sentence: str, level: str) -> Optional[Dict]:\n", " doc = SPACY_MODEL(sentence)\n", " words = [token.text for token in doc if token.text != '.']\n", " if len(words) < 3:\n", " print(\"Debug: Sentence too short for arrange question, skipping.\")\n", " error_counts[\"invalid_arrange\"] += 1\n", " return None\n", " random.shuffle(words)\n", " if ' '.join(words).strip().lower() == sentence.strip().lower():\n", " random.shuffle(words)\n", " return {\n", " \"question_text\": \"Arrange the words to form the correct sentence.\",\n", " \"words\": words,\n", " \"correct_sentence\": sentence\n", " }\n", "\n", " def create_labeling_question(sentence: str, level: str) -> Optional[Dict]:\n", " cleaned_sentence = clean_sentence(sentence)\n", " if cleaned_sentence is None:\n", " print(\"Debug: Invalid sentence for labeling question, skipping.\")\n", " return None\n", " doc = SPACY_MODEL(cleaned_sentence)\n", " root_count = sum(1 for token in doc if token.dep_ == \"ROOT\")\n", " if root_count > 1:\n", " print(\"Debug: Multiple clauses detected, skipping labeling question.\")\n", " error_counts[\"multiple_clauses\"] += 1\n", " return None\n", " is_passive = any(token.dep_ == \"auxpass\" for token in doc)\n", " if is_passive:\n", " print(\"Debug: Passive voice detected, skipping labeling question.\")\n", " error_counts[\"passive_voice\"] += 1\n", " return None\n", " constituents = {\n", " \"subject\": None,\n", " \"verb\": None,\n", " \"object\": None,\n", " \"adjective\": None,\n", " \"adverb\": None,\n", " \"prepositional phrase\": None\n", " }\n", " prep_obj = None\n", " main_verb = None\n", " for token in doc:\n", " if token.dep_ == \"nsubj\" and not constituents[\"subject\"]:\n", " constituents[\"subject\"] = token.text\n", " for child in token.children:\n", " if child.dep_ in [\"det\", \"compound\"]:\n", " constituents[\"subject\"] = f\"{child.text} {constituents['subject']}\"\n", " if token.dep_ == \"ROOT\" and token.pos_ in [\"VERB\", \"AUX\"]:\n", " if token.pos_ == \"AUX\" and not main_verb:\n", " for child in token.children:\n", " if child.pos_ == \"VERB\" and child.dep_ in [\"xcomp\", \"ccomp\", \"advcl\"]:\n", " main_verb = child.text\n", " break\n", " if not main_verb:\n", " main_verb = token.text\n", " if token.dep_ == \"dobj\" and not constituents[\"object\"]:\n", " obj = token.text\n", " for child in token.children:\n", " if child.dep_ in [\"det\", \"amod\", \"compound\"]:\n", " obj = f\"{child.text} {obj}\"\n", " constituents[\"object\"] = obj\n", " if token.dep_ in [\"amod\", \"acomp\"] and token.pos_ == \"ADJ\" and not constituents[\"adjective\"]:\n", " constituents[\"adjective\"] = token.text\n", " if token.dep_ == \"advmod\" and token.pos_ == \"ADV\" and not constituents[\"prepositional phrase\"]:\n", " for child in token.head.children:\n", " if child.dep_ == \"prep\" and child.text in [\"around\", \"about\"]:\n", " constituents[\"prepositional phrase\"] = f\"{child.text}\"\n", " for gc in child.children:\n", " if gc.dep_ == \"pobj\":\n", " constituents[\"prepositional phrase\"] += f\" {gc.text}\"\n", " break\n", " print(f\"Debug: Relabeled '{token.text}' as prepositional phrase\")\n", " break\n", " else:\n", " constituents[\"adverb\"] = token.text\n", " if token.dep_ == \"prep\" and not constituents[\"prepositional phrase\"]:\n", " for child in token.children:\n", " if child.dep_ == \"pobj\":\n", " prep_obj = child.text\n", " modifiers = []\n", " for grandchild in child.children:\n", " if grandchild.dep_ in [\"det\", \"amod\", \"nummod\", \"compound\"]:\n", " modifiers.append(grandchild.text)\n", " modifiers = sorted(modifiers, key=lambda x: doc.text.find(x))\n", " prep_obj = \" \".join(modifiers + [prep_obj]).strip()\n", " constituents[\"prepositional phrase\"] = f\"{token.text} {prep_obj}\"\n", " break\n", " constituents[\"verb\"] = main_verb\n", " if constituents[\"verb\"]:\n", " for contraction, expansion in CONTRACTION_MAP.items():\n", " if contraction in constituents[\"verb\"].lower():\n", " constituents[\"verb\"] = expansion\n", " valid_constituents = [k for k, v in constituents.items() if v is not None]\n", " if len(valid_constituents) < 3:\n", " print(f\"Debug: Insufficient constituents ({len(valid_constituents)}/3): \" +\n", " \", \".join(f\"{k} ({v if v else 'None'})\" for k, v in constituents.items()) +\n", " \", skipping labeling question.\")\n", " error_counts[\"insufficient_constituents\"] += 1\n", " return None\n", " valid_values = [v.lower() for v in constituents.values() if v is not None]\n", " if len(set(valid_values)) != len(valid_values):\n", " print(\"Debug: Constituents are not distinct, skipping labeling question.\")\n", " error_counts[\"no_valid_constituents\"] += 1\n", " return None\n", " correct_labels = {k: v for k, v in constituents.items() if v is not None}\n", " return {\n", " \"question_text\": cleaned_sentence,\n", " \"instruction\": f\"Assign {', '.join(correct_labels.keys())} to the correct words or phrases.\",\n", " \"correct_labels\": correct_labels\n", " }\n", "\n", " validated_sentences = []\n", " target_score = cefr_to_score.get(level, 1)\n", " attempt_count = 0\n", " max_attempts = 200\n", "\n", " while len(validated_sentences) < num_sentences and attempt_count < max_attempts:\n", " attempt_count += 1\n", " print(f\"Debug: Generating sentence attempt {attempt_count}/{max_attempts} for level {level}\")\n", " try:\n", " input_text = f\"### Input:\\nGenerate a sentence at CEFR level {level}\"\n", " if topic:\n", " input_text += f\" about {topic}\"\n", " input_text += \" Please don't use to be Verb and just output the sentence\\n### Response:\\n\"\n", " input_data = GENERATOR_TOKENIZER(\n", " input_text,\n", " return_tensors=\"pt\",\n", " padding=True,\n", " truncation=True,\n", " max_length=MAX_SEQ_LENGTH,\n", " ).to(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", " max_tokens_map = {\n", " 'A1': 30,\n", " 'A2': 30,\n", " 'B1': 35,\n", " 'B2': 35,\n", " 'C1': 50,\n", " 'C2': 60\n", " }\n", " max_new_tokens = max_tokens_map.get(level, 30)\n", " print(f\"Debug: Using max_new_tokens={max_new_tokens} for level {level}\")\n", " outputs = GENERATOR_MODEL.generate(\n", " input_ids=input_data[\"input_ids\"],\n", " attention_mask=input_data[\"attention_mask\"],\n", " max_new_tokens=max_new_tokens,\n", " pad_token_id=GENERATOR_TOKENIZER.pad_token_id,\n", " do_sample=True,\n", " temperature=0.8,\n", " top_k=60,\n", " top_p=0.9,\n", " )\n", " if not isinstance(outputs, torch.Tensor) or outputs.numel() == 0:\n", " print(\"Debug: Invalid model output, skipping.\")\n", " continue\n", " generated_text = GENERATOR_TOKENIZER.decode(outputs[0], skip_special_tokens=True)\n", " if not isinstance(generated_text, str) or not generated_text.strip():\n", " print(\"Debug: Invalid decoded text, skipping.\")\n", " continue\n", " response_start = generated_text.find(\"### Response:\\n\") + len(\"### Response:\\n\")\n", " response_text = generated_text[response_start:].strip()\n", " response_text = re.sub(r'^Example sentence at level [A-C][1-2]: ', '', response_text, flags=re.IGNORECASE)\n", " original_response = response_text\n", " print(f\"Debug: Raw model output: '{original_response}'\")\n", " response_text = clean_sentence(response_text)\n", " if response_text is None:\n", " error_counts[\"no_full_stop\"] += 1\n", " print(\"Debug: Sentence rejected due to missing full-stop, retrying.\")\n", " continue\n", " print(f\"Debug: Original sentence: '{original_response}'\")\n", " print(f\"Debug: Cleaned sentence: '{response_text}'\")\n", " if original_response != response_text:\n", " print(f\"Debug: Processed sentence: '{original_response}' -> '{response_text}'\")\n", " error_counts[\"output_pattern_removed\"] += 1\n", " for contraction, expansion in CONTRACTION_MAP.items():\n", " response_text = re.sub(rf'\\b(\\w+){contraction}\\b', r'\\1 ' + expansion, response_text, flags=re.IGNORECASE)\n", " doc = SPACY_MODEL(response_text)\n", " sents = list(doc.sents)\n", " first_sent = sents[0].text.strip() if sents else response_text\n", " if len(first_sent.split()) < 3:\n", " print(\"Debug: Sentence too short, skipping.\")\n", " continue\n", " doc = SPACY_MODEL(first_sent)\n", " verbs = []\n", " for token in doc:\n", " if token.pos_ in [\"VERB\", \"AUX\"] and token.dep_ in [\"ROOT\", \"cop\", \"aux\"]:\n", " if token.text.lower() == \"to\" and token.dep_ == \"aux\":\n", " continue\n", " if token.pos_ == \"AUX\" and token.dep_ == \"ROOT\":\n", " for child in token.children:\n", " if child.pos_ == \"VERB\" and child.dep_ in [\"xcomp\", \"ccomp\", \"advcl\"]:\n", " verbs.append(child.text)\n", " print(f\"Debug: Selected main verb '{child.text}' over modal '{token.text}'\")\n", " break\n", " else:\n", " verbs.append(token.text)\n", " else:\n", " verbs.append(token.text)\n", " if not verbs and \"'\" in first_sent:\n", " contractions = [token for token in doc if \"'\" in token.text and token.pos_ in [\"VERB\", \"AUX\"]]\n", " if contractions:\n", " verbs.append(contractions[0].text)\n", " verb = verbs[0] if verbs else None\n", " if not verb:\n", " error_counts[\"no_valid_verb\"] += 1\n", " print(\"Debug: No valid verb found, skipping.\")\n", " continue\n", " predicted_score = predict_sentiment(first_sent)\n", " if abs(predicted_score - target_score) > 1:\n", " error_counts[\"cefr_mismatch\"] += 1\n", " print(\"Debug: Sentence CEFR level mismatch, skipping.\")\n", " continue\n", " if any(existing[\"sentence\"] == first_sent for existing in validated_sentences):\n", " error_counts[\"duplicate_sentence\"] += 1\n", " print(\"Debug: Duplicate sentence, skipping.\")\n", " continue\n", " sentence_data = {\n", " \"sentence\": first_sent,\n", " \"verb\": verb,\n", " \"level\": level,\n", " \"for_user\": user_id\n", " }\n", " if \"selection\" in question_types:\n", " selection_question = create_selection_question(first_sent, level)\n", " sentence_data[\"selection_question\"] = selection_question\n", " if \"labeling\" in question_types:\n", " labeling_question = create_labeling_question(first_sent, level)\n", " sentence_data[\"labeling_question\"] = labeling_question\n", " if \"fill_in_blank\" in question_types:\n", " fill_in_blank_question = create_fill_in_blank(first_sent, level)\n", " sentence_data[\"fill_in_blank_question\"] = fill_in_blank_question\n", " if \"multiple_choice\" in question_types:\n", " multiple_choice_question = create_multiple_choice(first_sent, level)\n", " sentence_data[\"multiple_choice_question\"] = multiple_choice_question\n", " if \"arrange\" in question_types:\n", " arrange_question = create_arrange_question(first_sent, level)\n", " sentence_data[\"arrange_question\"] = arrange_question\n", " if not any([sentence_data.get(qt + \"_question\") for qt in question_types]):\n", " print(\"Debug: No valid questions generated, skipping.\")\n", " continue\n", " validated_sentences.append(sentence_data)\n", " print(f\"Debug: Added valid sentence: '{first_sent}'\")\n", " except Exception as e:\n", " print(f\"Error generating sentence: {str(e)}\")\n", " continue\n", "\n", " if not validated_sentences:\n", " print(f\"Warning: No valid sentences generated after {attempt_count} attempts.\")\n", "\n", " print(\"\\nError Statistics:\")\n", " print(f\"Duplicate sentence = {error_counts['duplicate_sentence']} Case\")\n", " print(f\"Sentence CEFR level mismatch = {error_counts['cefr_mismatch']} Case\")\n", " print(f\"No valid verb found = {error_counts['no_valid_verb']} Case\")\n", " print(f\"No valid constituents = {error_counts['no_valid_constituents']} Case\")\n", " print(f\"Insufficient constituents = {error_counts['insufficient_constituents']} Case\")\n", " print(f\"Multiple clauses = {error_counts['multiple_clauses']} Case\")\n", " print(f\"Passive voice = {error_counts['passive_voice']} Case\")\n", " print(f\"Duplicate selection sentences = {error_counts['duplicate_selection']} Case\")\n", " print(f\"Invalid incorrect sentences = {error_counts['invalid_incorrect_sentence']} Case\")\n", " print(f\"Invalid fill-in-the-blank = {error_counts['invalid_fill_in_blank']} Case\")\n", " print(f\"Invalid multiple choice = {error_counts['invalid_multiple_choice']} Case\")\n", " print(f\"Invalid arrange questions = {error_counts['invalid_arrange']} Case\")\n", " print(f\"No full-stop in sentence = {error_counts['no_full_stop']} Case\")\n", "\n", " return validated_sentences\n", "\n", "# Evaluation functions\n", "def evaluate_selection(selected: str, correct: str) -> Dict:\n", " selected = clean_sentence(selected)\n", " if selected is None:\n", " return {\n", " \"is_correct\": False,\n", " \"score\": 0.0,\n", " \"message\": \"Invalid selected sentence: no full-stop found.\"\n", " }\n", " correct = clean_sentence(correct)\n", " if correct is None:\n", " return {\n", " \"is_correct\": False,\n", " \"score\": 0.0,\n", " \"message\": \"Invalid correct sentence: no full-stop found.\"\n", " }\n", " is_correct = selected.strip() == correct.strip()\n", " score = 1.0 if is_correct else 0.0\n", " return {\n", " \"is_correct\": is_correct,\n", " \"score\": score,\n", " \"message\": \"Selection is correct.\" if is_correct else \"Selection is incorrect.\"\n", " }\n", "\n", "def evaluate_labeling(sentence: str, user_labels: Dict[str, str]) -> Dict:\n", " global SPACY_MODEL\n", " cleaned_sentence = clean_sentence(sentence)\n", " if cleaned_sentence is None:\n", " return {\n", " \"is_correct\": False,\n", " \"score\": 0.0,\n", " \"message\": \"Invalid sentence: no full-stop found.\"\n", " }\n", " doc = SPACY_MODEL(cleaned_sentence)\n", " correct_labels = {\n", " \"subject\": None,\n", " \"verb\": None,\n", " \"object\": None,\n", " \"adjective\": None,\n", " \"adverb\": None,\n", " \"prepositional phrase\": None\n", " }\n", " prep_obj = None\n", " main_verb = None\n", " for token in doc:\n", " if token.dep_ == \"nsubj\" and not correct_labels[\"subject\"]:\n", " correct_labels[\"subject\"] = token.text\n", " for child in token.children:\n", " if child.dep_ in [\"det\", \"compound\"]:\n", " correct_labels[\"subject\"] = f\"{child.text} {correct_labels['subject']}\"\n", " if token.dep_ == \"ROOT\" and token.pos_ in [\"VERB\", \"AUX\"]:\n", " if token.pos_ == \"AUX\" and not main_verb:\n", " for child in token.children:\n", " if child.pos_ == \"VERB\" and child.dep_ in [\"xcomp\", \"ccomp\", \"advcl\"]:\n", " main_verb = child.text\n", " break\n", " if not main_verb:\n", " main_verb = token.text\n", " if token.dep_ == \"dobj\" and not correct_labels[\"object\"]:\n", " obj = token.text\n", " for child in token.children:\n", " if child.dep_ in [\"det\", \"amod\", \"compound\"]:\n", " obj = f\"{child.text} {obj}\"\n", " correct_labels[\"object\"] = obj\n", " if token.dep_ in [\"amod\", \"acomp\"] and token.pos_ == \"ADJ\" and not correct_labels[\"adjective\"]:\n", " correct_labels[\"adjective\"] = token.text\n", " if token.dep_ == \"advmod\" and token.pos_ == \"ADV\" and not correct_labels[\"prepositional phrase\"]:\n", " for child in token.head.children:\n", " if child.dep_ == \"prep\" and child.text in [\"around\", \"about\"]:\n", " correct_labels[\"prepositional phrase\"] = f\"{child.text}\"\n", " for gc in child.children:\n", " if gc.dep_ == \"pobj\":\n", " correct_labels[\"prepositional phrase\"] += f\" {gc.text}\"\n", " break\n", " print(f\"Debug: Relabeled '{token.text}' as prepositional phrase\")\n", " break\n", " else:\n", " correct_labels[\"adverb\"] = token.text\n", " if token.dep_ == \"prep\" and not correct_labels[\"prepositional phrase\"]:\n", " for child in token.children:\n", " if child.dep_ == \"pobj\":\n", " prep_obj = child.text\n", " for grandchild in child.children:\n", " if grandchild.dep_ in [\"det\", \"amod\"]:\n", " prep_obj = f\"{grandchild.text} {prep_obj}\"\n", " correct_labels[\"prepositional phrase\"] = f\"{token.text} {prep_obj}\"\n", " break\n", " correct_labels[\"verb\"] = main_verb\n", " if correct_labels[\"verb\"]:\n", " for contraction, expansion in {\n", " \"'ve\": \"have\",\n", " \"'s\": \"is\",\n", " \"'re\": \"are\",\n", " \"'m\": \"am\",\n", " \"'ll\": \"will\",\n", " \"'d\": \"had\"\n", " }.items():\n", " if contraction in correct_labels[\"verb\"].lower():\n", " correct_labels[\"verb\"] = expansion\n", " valid_correct_labels = {k: v for k, v in correct_labels.items() if v is not None}\n", " if not valid_correct_labels:\n", " return {\n", " \"is_correct\": False,\n", " \"score\": 0.0,\n", " \"message\": \"No valid constituents found in sentence.\"\n", " }\n", " score_per_label = 1.0 / len(valid_correct_labels)\n", " is_correct = all(\n", " user_labels.get(key, \"\").strip().lower() == value.strip().lower()\n", " for key, value in valid_correct_labels.items()\n", " )\n", " score = sum(\n", " score_per_label\n", " for key, value in valid_correct_labels.items()\n", " if user_labels.get(key, \"\").strip().lower() == value.strip().lower()\n", " )\n", " return {\n", " \"is_correct\": is_correct,\n", " \"score\": round(score, 2),\n", " \"message\": \"Labeling is correct.\" if is_correct else \"Labeling is incorrect.\"\n", " }\n", "\n", "def evaluate_fill_in_blank(user_answer: str, correct_answer: str) -> Dict:\n", " is_correct = user_answer.strip().lower() == correct_answer.strip().lower()\n", " score = 1.0 if is_correct else 0.0\n", " return {\n", " \"is_correct\": is_correct,\n", " \"score\": score,\n", " \"message\": \"Answer is correct.\" if is_correct else \"Answer is incorrect.\"\n", " }\n", "\n", "def evaluate_multiple_choice(selected_option: str, correct_answer: str) -> Dict:\n", " is_correct = selected_option.strip().lower() == correct_answer.strip().lower()\n", " score = 1.0 if is_correct else 0.0\n", " return {\n", " \"is_correct\": is_correct,\n", " \"score\": score,\n", " \"message\": \"Selection is correct.\" if is_correct else \"Selection is incorrect.\"\n", " }\n", "\n", "def evaluate_arrange(user_arrangement: List[str], correct_sentence: str) -> Dict:\n", " user_sentence = \" \".join(user_arrangement).strip()\n", " correct_sentence = clean_sentence(correct_sentence)\n", " if correct_sentence is None:\n", " return {\n", " \"is_correct\": False,\n", " \"score\": 0.0,\n", " \"message\": \"Invalid correct sentence: no full-stop found.\"\n", " }\n", " is_correct = user_sentence.lower() == correct_sentence.lower()\n", " score = 1.0 if is_correct else 0.0\n", " return {\n", " \"is_correct\": is_correct,\n", " \"score\": score,\n", " \"message\": \"Arrangement is correct.\" if is_correct else \"Arrangement is incorrect.\"\n", " }\n", "\n", "# API endpoints\n", "@app.post(\"/generate_sentences\", response_model=SentenceResponse)\n", "async def generate_sentences(request: Request, sentence_request: SentenceRequest):\n", " start_time = time.time()\n", " try:\n", " level = sentence_request.level\n", " num_sentences = sentence_request.num_sentences\n", " user_id = sentence_request.user_id\n", " topic = sentence_request.topic\n", " question_types = sentence_request.question_types\n", " valid_question_types = [\"selection\", \"labeling\", \"fill_in_blank\", \"multiple_choice\", \"arrange\"]\n", " if not all(qt in valid_question_types for qt in question_types):\n", " raise HTTPException(status_code=400, detail=f\"Invalid question types. Must be subset of: {valid_question_types}\")\n", " if level not in [\"A1\", \"A2\", \"B1\", \"B2\", \"C1\", \"C2\"]:\n", " raise HTTPException(status_code=400, detail=\"Invalid level. Must be one of: A1, A2, B1, B2, C1, C2\")\n", " if num_sentences < 1 or num_sentences > 10:\n", " raise HTTPException(status_code=400, detail=\"Number of sentences must be between 1 and 10\")\n", " sentences = generate_sentences_internal(level, num_sentences, user_id, topic, question_types)\n", " processing_time = time.time() - start_time\n", " current_time = datetime.datetime.now().isoformat()\n", " if not sentences:\n", " print(\"Debug: No sentences generated, returning empty response\")\n", " return {\n", " \"success\": False,\n", " \"sentences\": [],\n", " \"message\": \"Failed to generate sentences. Please try again.\",\n", " \"processing_time\": processing_time,\n", " \"timestamp\": current_time\n", " }\n", " response = {\n", " \"success\": True,\n", " \"sentences\": sentences,\n", " \"message\": f\"Generated {len(sentences)} sentences for level {level}\" +\n", " (f\" on topic '{topic}'\" if topic else \"\") +\n", " f\" with question types {question_types}\",\n", " \"processing_time\": processing_time,\n", " \"timestamp\": current_time\n", " }\n", " print(f\"Debug: Sending response: {response}\")\n", " return response\n", " except HTTPException:\n", " raise\n", " except Exception as e:\n", " processing_time = time.time() - start_time\n", " current_time = datetime.datetime.now().isoformat()\n", " print(f\"API error: {str(e)}\")\n", " return {\n", " \"success\": False,\n", " \"sentences\": [],\n", " \"message\": f\"Internal server error: {str(e)}\",\n", " \"processing_time\": processing_time,\n", " \"timestamp\": current_time\n", " }\n", "\n", "@app.post(\"/evaluate_selection\", response_model=SelectionResponse)\n", "async def evaluate_selection(request: Request, selection_request: SelectionRequest):\n", " start_time = time.time()\n", " try:\n", " result = evaluate_selection(\n", " selection_request.selected_sentence,\n", " selection_request.correct_sentence\n", " )\n", " processing_time = time.time() - start_time\n", " current_time = datetime.datetime.now().isoformat()\n", " return {\n", " \"success\": True,\n", " \"is_correct\": result[\"is_correct\"],\n", " \"score\": result[\"score\"],\n", " \"message\": result[\"message\"],\n", " \"processing_time\": processing_time,\n", " \"timestamp\": current_time\n", " }\n", " except Exception as e:\n", " processing_time = time.time() - start_time\n", " current_time = datetime.datetime.now().isoformat()\n", " return {\n", " \"success\": False,\n", " \"is_correct\": False,\n", " \"score\": 0.0,\n", " \"message\": f\"Error evaluating selection: {str(e)}\",\n", " \"processing_time\": processing_time,\n", " \"timestamp\": current_time\n", " }\n", "\n", "def run_server(port: int = 8000, max_attempts: int = 5):\n", " import socket\n", " for attempt in range(max_attempts):\n", " try:\n", " with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n", " s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n", " s.bind((\"0.0.0.0\", port))\n", " config = uvicorn.Config(\n", " app,\n", " host=\"0.0.0.0\",\n", " port=port,\n", " log_level=\"info\",\n", " timeout_keep_alive=30\n", " )\n", " server = uvicorn.Server(config)\n", " print(f\"Starting server on port {port}...\")\n", " asyncio.run(server.serve())\n", " break\n", " except OSError as e:\n", " if \"address already in use\" in str(e).lower() and attempt < max_attempts - 1:\n", " print(f\"Port {port} in use, trying port {port + 1}...\")\n", " port += 1\n", " time.sleep(1)\n", " else:\n", " print(f\"Failed to start server: {str(e)}\")\n", " raise\n", " except Exception as e:\n", " print(f\"Server error: {str(e)}\")\n", " raise\n", "\n", "def start_ngrok(port: int) -> Optional[str]:\n", " try:\n", " ngrok.kill()\n", " ngrok.set_auth_token(\"2oFl98p58vTuHbdQu5m2t1exmDW_5MXsrmeuZtv9J5hTscGzb\")\n", " public_url = ngrok.connect(port, bind_tls=True).public_url\n", " print(f\"Ngrok tunnel created: {public_url}\")\n", " return public_url\n", " except Exception as e:\n", " print(f\"Ngrok error: {str(e)}\")\n", " print(\"Continuing without ngrok - you'll need to access the API locally\")\n", " return None\n", "\n", "def start_application():\n", " try:\n", " load_models()\n", " except Exception as e:\n", " print(f\"Failed to load models: {str(e)}\")\n", " return\n", " port = 8000\n", " max_attempts = 5\n", " attempt = 0\n", " while attempt < max_attempts:\n", " try:\n", " server_thread = threading.Thread(target=run_server, args=(port,))\n", " server_thread.daemon = True\n", " server_thread.start()\n", " time.sleep(2)\n", " public_url = start_ngrok(port)\n", " if public_url:\n", " print(f\"\\nAPI is available at: {public_url}/generate_sentences\")\n", " else:\n", " print(f\"\\nAPI is running locally at: http://localhost:{port}/generate_sentences\")\n", " while True:\n", " time.sleep(1)\n", " except KeyboardInterrupt:\n", " print(\"\\nShutting down server...\")\n", " ngrok.kill()\n", " import os\n", " os._exit(0)\n", " except Exception as e:\n", " print(f\"Application error: {str(e)}\")\n", " ngrok.kill()\n", " attempt += 1\n", " port += 1\n", " time.sleep(2)\n", " if attempt < max_attempts:\n", " print(f\"Retrying with port {port}...\")\n", "\n", "if __name__ == \"__main__\":\n", " start_application()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from fastapi import FastAPI, HTTPException, Request\n", "from pydantic import BaseModel\n", "import uvicorn\n", "from unsloth import FastLanguageModel\n", "from sentence_transformers import SentenceTransformer\n", "import joblib\n", "import numpy as np\n", "import re\n", "import random\n", "from huggingface_hub import snapshot_download\n", "import torch\n", "import spacy\n", "from pyngrok import ngrok\n", "import nest_asyncio\n", "import threading\n", "import asyncio\n", "import time\n", "from typing import List, Dict, Optional\n", "from transformers import StoppingCriteria\n", "import datetime\n", "import os\n", "\n", "# Allow nested event loops for Colab\n", "nest_asyncio.apply()\n", "\n", "# Define FastAPI app\n", "app = FastAPI()\n", "\n", "# Global variables for loaded models and constants\n", "GENERATOR_MODEL = None\n", "GENERATOR_TOKENIZER = None\n", "DISTRACTOR_MODEL = None\n", "DISTRACTOR_TOKENIZER = None\n", "EVALUATOR_MODEL = None\n", "EMBEDDER_MODEL = None\n", "SPACY_MODEL = None\n", "MAX_SEQ_LENGTH = 2048\n", "\n", "# Define request models\n", "class SentenceRequest(BaseModel):\n", " level: str\n", " num_sentences: int = 1\n", " user_id: int\n", " topic: Optional[str] = None\n", " question_types: Optional[List[str]] = [\"selection\", \"labeling\", \"fill_in_blank\", \"multiple_choice\", \"arrange\"]\n", "\n", "class SelectionRequest(BaseModel):\n", " selected_sentence: str\n", " sentences: str # Changed to str to match the | delimiter\n", " correct_sentence: str\n", " target_level: str\n", " user_id: int\n", "\n", "class LabelingRequest(BaseModel):\n", " sentence: str\n", " user_labels: Dict[str, str]\n", " target_level: str\n", " user_id: int\n", "\n", "class FillInBlankRequest(BaseModel):\n", " sentence: str\n", " user_answer: str\n", " correct_answer: str\n", " target_level: str\n", " user_id: int\n", "\n", "class MultipleChoiceRequest(BaseModel):\n", " selected_option: str\n", " options: List[str]\n", " correct_answer: str\n", " target_level: str\n", " user_id: int\n", "\n", "class ArrangeRequest(BaseModel):\n", " sentence: str\n", " user_arrangement: List[str]\n", " correct_sentence: str\n", " target_level: str\n", " user_id: int\n", "\n", "# Define response models\n", "class SentenceResponse(BaseModel):\n", " success: bool\n", " sentences: List[Dict]\n", " message: str\n", " processing_time: float\n", " timestamp: str\n", "\n", "class SelectionResponse(BaseModel):\n", " success: bool\n", " is_correct: bool\n", " score: float\n", " message: str\n", " processing_time: float\n", " timestamp: str\n", "\n", "class LabelingResponse(BaseModel):\n", " success: bool\n", " is_correct: bool\n", " score: float\n", " message: str\n", " processing_time: float\n", " timestamp: str\n", "\n", "class FillInBlankResponse(BaseModel):\n", " success: bool\n", " is_correct: bool\n", " score: float\n", " message: str\n", " processing_time: float\n", " timestamp: str\n", "\n", "class MultipleChoiceResponse(BaseModel):\n", " success: bool\n", " is_correct: bool\n", " score: float\n", " message: str\n", " processing_time: float\n", " timestamp: str\n", "\n", "class ArrangeResponse(BaseModel):\n", " success: bool\n", " is_correct: bool\n", " score: float\n", " message: str\n", " processing_time: float\n", " timestamp: str\n", "\n", "class StopOnTokens(StoppingCriteria):\n", " def __init__(self, tokenizer, stop_strings):\n", " self.tokenizer = tokenizer\n", " self.stop_strings = stop_strings\n", " self.stop_ids = [tokenizer.encode(s, add_special_tokens=False) for s in stop_strings]\n", "\n", " def __call__(self, input_ids, scores, **kwargs):\n", " for stop_id in self.stop_ids:\n", " if len(input_ids[0]) >= len(stop_id):\n", " if all(input_ids[0][-len(stop_id):] == stop_id):\n", " return True\n", " return False\n", "\n", "# Model loading function\n", "def load_models():\n", " global GENERATOR_MODEL, GENERATOR_TOKENIZER, DISTRACTOR_MODEL, DISTRACTOR_TOKENIZER, EVALUATOR_MODEL, EMBEDDER_MODEL, SPACY_MODEL\n", " print(\"Loading all models...\")\n", " dtype = None\n", " load_in_4bit = True\n", " # generator_model_name = \"Mr-FineTuner/Test_02_llama_1epoch_trainPercen_myValidator_fix\"\n", " generator_model_name = \"Mr-FineTuner/With_synthetic_Dataset_llama-1epoch\"\n", " try:\n", " GENERATOR_MODEL, GENERATOR_TOKENIZER = FastLanguageModel.from_pretrained(\n", " model_name=generator_model_name,\n", " max_seq_length=MAX_SEQ_LENGTH,\n", " dtype=dtype,\n", " load_in_4bit=load_in_4bit,\n", " )\n", " if GENERATOR_TOKENIZER.pad_token is None:\n", " GENERATOR_TOKENIZER.pad_token = GENERATOR_TOKENIZER.eos_token\n", " GENERATOR_MODEL = FastLanguageModel.for_inference(GENERATOR_MODEL)\n", " print(f\"Finetuned generator model '{generator_model_name}' loaded successfully!\")\n", " except Exception as e:\n", " print(f\"Error loading finetuned generator model: {str(e)}\")\n", " raise\n", " print(\"Loading plain distractor model...\")\n", " distractor_model_name = \"unsloth/llama-3-8b-Instruct-bnb-4bit\"\n", " try:\n", " DISTRACTOR_MODEL, DISTRACTOR_TOKENIZER = FastLanguageModel.from_pretrained(\n", " model_name=distractor_model_name,\n", " max_seq_length=MAX_SEQ_LENGTH,\n", " dtype=dtype,\n", " load_in_4bit=load_in_4bit,\n", " )\n", " if DISTRACTOR_TOKENIZER.pad_token is None:\n", " DISTRACTOR_TOKENIZER.pad_token = DISTRACTOR_TOKENIZER.eos_token\n", " DISTRACTOR_MODEL = FastLanguageModel.for_inference(DISTRACTOR_MODEL)\n", " print(f\"Plain distractor model '{distractor_model_name}' loaded successfully!\")\n", " except Exception as e:\n", " print(f\"Error loading plain distractor model: {str(e)}\")\n", " raise\n", " print(\"Loading validator model...\")\n", " try:\n", " eval_model_path = snapshot_download(\n", " repo_id=\"Mr-FineTuner/Skripsi_validator_best_model\",\n", " allow_patterns=[\"model_mlp.joblib\"],\n", " local_dir=\"validator_model\"\n", " )\n", " classifier_path = os.path.join(eval_model_path, \"model_mlp.joblib\")\n", " if not os.path.exists(classifier_path):\n", " raise FileNotFoundError(f\"model_mlp.joblib not found in {eval_model_path}\")\n", " EVALUATOR_MODEL = joblib.load(classifier_path)\n", " EMBEDDER_MODEL = SentenceTransformer('BAAI/bge-base-en-v1.5')\n", " print(\"Validator models loaded successfully!\")\n", " except Exception as e:\n", " print(f\"Error loading validator models: {str(e)}\")\n", " raise\n", " print(\"Loading spaCy model...\")\n", " try:\n", " SPACY_MODEL = spacy.load(\"en_core_web_sm\")\n", " print(\"spaCy model loaded successfully!\")\n", " except Exception as e:\n", " print(f\"Error loading spaCy model: {str(e)}\")\n", " raise\n", "\n", "# Sentence generation logic\n", "def generate_sentences_internal(level: str, num_sentences: int, user_id: int, topic: Optional[str] = None, question_types: List[str] = [\"selection\", \"labeling\", \"fill_in_blank\", \"multiple_choice\", \"arrange\"]) -> List[Dict]:\n", " global GENERATOR_MODEL, GENERATOR_TOKENIZER, DISTRACTOR_MODEL, DISTRACTOR_TOKENIZER, EVALUATOR_MODEL, EMBEDDER_MODEL, SPACY_MODEL\n", " if None in [GENERATOR_MODEL, GENERATOR_TOKENIZER, DISTRACTOR_MODEL, DISTRACTOR_TOKENIZER, EVALUATOR_MODEL, EMBEDDER_MODEL, SPACY_MODEL]:\n", " raise ValueError(\"One or more models failed to load\")\n", "\n", " # Contraction mapping for expansion\n", " CONTRACTION_MAP = {\n", " \"'ve\": \"have\",\n", " \"'s\": \"is\",\n", " \"'re\": \"are\",\n", " \"'m\": \"am\",\n", " \"'ll\": \"will\",\n", " \"'d\": \"had\"\n", " }\n", "\n", " error_counts = {\n", " \"duplicate_sentence\": 0,\n", " \"cefr_mismatch\": 0,\n", " \"no_valid_verb\": 0,\n", " \"no_valid_constituents\": 0,\n", " \"insufficient_constituents\": 0,\n", " \"multiple_clauses\": 0,\n", " \"passive_voice\": 0,\n", " \"output_pattern_removed\": 0,\n", " \"duplicate_selection\": 0,\n", " \"invalid_incorrect_sentence\": 0,\n", " \"invalid_fill_in_blank\": 0,\n", " \"invalid_multiple_choice\": 0,\n", " \"invalid_arrange\": 0,\n", " \"no_full_stop\": 0\n", " }\n", "\n", " def predict_sentiment(text: str) -> int:\n", " embedding = EMBEDDER_MODEL.encode([text])\n", " probs = EVALUATOR_MODEL.predict_proba(embedding)[0]\n", " prediction = np.argmax(probs) + 1\n", " return max(1, min(6, int(prediction)))\n", "\n", " cefr_to_score = {'A1': 1, 'A2': 2, 'B1': 3, 'B2': 4, 'C1': 5, 'C2': 6}\n", " score_to_cefr = {1: 'A1', 2: 'A2', 3: 'B1', 4: 'B2', 5: 'C1', 6: 'C2'}\n", "\n", " def clean_sentence(text: str) -> Optional[str]:\n", " cleaned = re.sub(r'\\n+#+\\s*Output\\s*:', '', text, flags=re.IGNORECASE)\n", " cleaned = re.sub(r'\\n+\\s*###\\s*Output\\s*:', '', cleaned, flags=re.IGNORECASE)\n", " cleaned = cleaned.strip()\n", " match = re.match(r'^(.*?)\\.', cleaned)\n", " if match:\n", " cleaned = match.group(1) + '.'\n", " else:\n", " print(\"Debug: No full-stop found in sentence, rejecting.\")\n", " return None\n", " cleaned = cleaned.strip()\n", " if not cleaned:\n", " print(\"Debug: Cleaned sentence is empty, rejecting.\")\n", " return None\n", " return cleaned\n", "\n", " def is_article_correct(sent: str, orig_doc) -> bool:\n", " doc = SPACY_MODEL(sent)\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"nsubj\" and i > 0:\n", " prev = doc[i-1].text.lower()\n", " if prev in [\"a\", \"an\"]:\n", " first_letter = orig_doc[i].text[0].lower()\n", " is_vowel = first_letter in 'aeiou'\n", " if (prev == \"a\" and is_vowel) or (prev == \"an\" and not is_vowel):\n", " return False\n", " elif prev == \"the\":\n", " return False\n", " elif token.dep_ == \"nsubj\" and i == 0:\n", " if token.tag_ in [\"NN\", \"NNP\"]:\n", " return False\n", " return True\n", "\n", " def is_subject_verb_agreement_correct(sent: str) -> bool:\n", " doc = SPACY_MODEL(sent)\n", " subject = None\n", " verb = None\n", " for token in doc:\n", " if token.dep_ == \"nsubj\":\n", " subject = token\n", " if token.dep_ == \"ROOT\" and token.pos_ in [\"VERB\", \"AUX\"]:\n", " verb = token\n", " if subject and verb:\n", " is_subject_plural = subject.tag_ in [\"NNS\", \"NNPS\"]\n", " is_verb_singular = verb.text.lower() in [\"is\", \"was\", \"has\"]\n", " return not (is_subject_plural != is_verb_singular)\n", " return True\n", "\n", " def create_selection_question(sentence: str, level: str) -> Dict:\n", " error_types = {\n", " 'A1': [\"spelling\", \"article\", \"number_agreement\"],\n", " 'A2': [\"spelling\", \"article\", \"tense\"],\n", " 'B1': [\"tense\", \"preposition\", \"word_choice\"],\n", " 'B2': [\"tense\", \"preposition\", \"agreement\"],\n", " 'C1': [\"complex_tense\", \"idiom\", \"word_form\"],\n", " 'C2': [\"complex_tense\", \"idiom\", \"subjunctive\"]\n", " }\n", " available_errors = error_types.get(level, error_types['B1'])\n", " if len(available_errors) < 2:\n", " available_errors = available_errors + [\"spelling\"]\n", " errors = random.sample(available_errors, 2)\n", "\n", " doc = SPACY_MODEL(sentence)\n", " words = [token.text for token in doc]\n", "\n", " def introduce_error(sent: str, error_type: str) -> str:\n", " doc = SPACY_MODEL(sent)\n", " words = [token.text for token in doc]\n", " modified = sent\n", " if error_type == \"spelling\":\n", " for i, word in enumerate(words):\n", " if len(word) > 3 and word.isalpha():\n", " if len(word) > 4:\n", " words[i] = word[:-2] + random.choice('ae') + random.choice('mn')\n", " else:\n", " words[i] = word[:-1] + random.choice('ae')\n", " modified = \" \".join(words).strip()\n", " if SPACY_MODEL(words[i])[0].is_oov:\n", " break\n", " else:\n", " modified = sent\n", " elif error_type == \"article\":\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"nsubj\" and i > 0 and words[i-1].lower() in [\"a\", \"an\"]:\n", " words[i-1] = \"\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif token.dep_ == \"nsubj\":\n", " first_letter = token.text[0].lower()\n", " is_vowel = first_letter in 'aeiou'\n", " words[i] = f\"{'a' if is_vowel else 'an'} {words[i]}\"\n", " modified = \" \".join(words).strip()\n", " break\n", " else:\n", " modified = sent\n", " elif error_type == \"number_agreement\":\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"nsubj\":\n", " is_plural = token.tag_ in [\"NNS\", \"NNPS\"]\n", " if is_plural:\n", " words[i] = SPACY_MODEL(token.lemma_)[0].text\n", " else:\n", " words[i] = token.text + \"s\"\n", " for j, t in enumerate(doc):\n", " if t.dep_ == \"ROOT\" and t.text.lower() in [\"is\", \"are\"]:\n", " words[j] = \"are\" if t.text.lower() == \"is\" else \"is\"\n", " break\n", " modified = \" \".join(words).strip()\n", " break\n", " else:\n", " modified = sent\n", " elif error_type == \"tense\":\n", " for i, token in enumerate(doc):\n", " if token.pos_ == \"VERB\" and token.text.lower() not in [\"be\", \"can\"]:\n", " words[i] = SPACY_MODEL(token.lemma_)[0].text + \"ed\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"preposition\":\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"prep\":\n", " words[i] = \"to\" if token.text != \"to\" else \"in\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"word_choice\":\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"dobj\":\n", " words[i] = \"banana\" if token.text.lower() != \"banana\" else \"orange\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"agreement\":\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"nsubj\" and token.text.lower() == \"i\":\n", " words[i] = \"We\"\n", " for v in doc:\n", " if v.pos_ == \"VERB\" and v.text.lower() in [\"like\", \"eat\"]:\n", " words[v.i] = v.text + \"s\"\n", " break\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"complex_tense\":\n", " for i, token in enumerate(doc):\n", " if token.pos_ == \"VERB\":\n", " words[i] = f\"had {token.lemma_}ed\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"idiom\":\n", " for i, token in enumerate(doc):\n", " if token.dep_ == \"dobj\":\n", " words[i] = \"moon\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"word_form\":\n", " for i, token in enumerate(doc):\n", " if token.pos_ == \"VERB\":\n", " words[i] = token.lemma_ + \"ing\"\n", " modified = \" \".join(words).strip()\n", " break\n", " elif error_type == \"subjunctive\":\n", " for i, token in enumerate(doc):\n", " if token.pos_ == \"VERB\":\n", " words[i] = token.lemma_\n", " modified = \" \".join(words).strip()\n", " break\n", " modified = clean_sentence(modified)\n", " if modified is None or modified.strip() == sent.strip():\n", " for i, word in enumerate(words):\n", " if len(word) > 3 and word.isalpha():\n", " words[i] = word[:-1] + random.choice('ae')\n", " modified = clean_sentence(\" \".join(words).strip())\n", " break\n", " return modified if modified else sent\n", "\n", " incorrect1 = introduce_error(sentence, errors[0])\n", " incorrect2 = introduce_error(sentence, errors[1])\n", " max_error_retries = 3\n", " error_retry_count = 0\n", " while (incorrect2 == incorrect1 or incorrect2 == sentence) and error_retry_count < max_error_retries:\n", " new_error = random.choice([e for e in available_errors if e != errors[0]])\n", " incorrect2 = introduce_error(sentence, new_error)\n", " errors[1] = new_error\n", " error_retry_count += 1\n", " if incorrect2 == incorrect1 or incorrect2 == sentence:\n", " for i, word in enumerate(words):\n", " if len(word) > 3 and word.isalpha():\n", " words[i] = word[:-1] + 'u'\n", " incorrect2 = clean_sentence(\" \".join(words).strip())\n", " errors[1] = \"spelling\"\n", " break\n", " sentences = [sentence, incorrect1, incorrect2]\n", " for i, s in enumerate(sentences):\n", " if s != sentence:\n", " is_valid = True\n", " try:\n", " if errors[i-1] == \"article\" and is_article_correct(s, doc):\n", " is_valid = False\n", " elif errors[i-1] == \"number_agreement\" and is_subject_verb_agreement_correct(s):\n", " is_valid = False\n", " elif errors[i-1] == \"spelling\":\n", " s_doc = SPACY_MODEL(s)\n", " for token, orig_token in zip(s_doc, doc):\n", " if token.text != orig_token.text and not token.is_oov:\n", " is_valid = False\n", " break\n", " except Exception as e:\n", " print(f\"Debug: Validation error for '{s}': {str(e)}\")\n", " is_valid = False\n", " if not is_valid:\n", " print(f\"Debug: Invalid incorrect sentence '{s}' for error '{errors[i-1]}'\")\n", " error_counts[\"invalid_incorrect_sentence\"] += 1\n", " new_error = random.choice([e for e in available_errors if e != errors[i-1]])\n", " sentences[i] = introduce_error(sentence, new_error)\n", " errors[i-1] = new_error\n", " if len(set(sentences)) != 3:\n", " print(f\"Debug: Duplicate sentences detected in selection question: {sentences}\")\n", " error_counts[\"duplicate_selection\"] += 1\n", " incorrect2 = introduce_error(sentence, random.choice(available_errors))\n", " sentences = [sentence, incorrect1, incorrect2]\n", " if len(set(sentences)) != 3:\n", " for i, word in enumerate(words):\n", " if len(word) > 3 and word.isalpha():\n", " words[i] = word[:-1] + 'u'\n", " incorrect2 = clean_sentence(\" \".join(words).strip())\n", " sentences = [sentence, incorrect1, incorrect2]\n", " break\n", " random.shuffle(sentences)\n", " return {\n", " \"question_text\": \"Choose the correct sentence.\",\n", " \"sentences\": \"|\".join(sentences), # Use | as delimiter\n", " \"correct_sentence\": sentence\n", " }\n", "\n", " def create_fill_in_blank(sentence: str, level: str) -> Optional[Dict]:\n", " doc = SPACY_MODEL(sentence)\n", " verb = None\n", " verb_index = None\n", " verb_lemma = None\n", " for i, token in enumerate(doc):\n", " if token.pos_ in [\"VERB\", \"AUX\"] and token.dep_ in [\"ROOT\", \"cop\"]:\n", " if token.text.lower() not in [\"to\"]:\n", " verb = token.text\n", " verb_index = i\n", " verb_lemma = token.lemma_.lower()\n", " break\n", " if not verb or not verb_lemma:\n", " print(\"Debug: No valid verb for fill-in-the-blank, skipping.\")\n", " error_counts[\"invalid_fill_in_blank\"] += 1\n", " return None\n", " words = [token.text for token in doc]\n", " words[verb_index] = \"__\"\n", " blanked_sentence = \" \".join(words).strip()\n", " question_text = f\"{blanked_sentence[:-1]} ({verb_lemma}).\"\n", " return {\n", " \"question_text\": question_text,\n", " \"correct_answer\": verb\n", " }\n", "\n", " def generate_contextual_distractors(target_verb: str, sentence: str, num_distractors: int = 3) -> List[str]:\n", " print(f\"Debug: Starting distractor generation for verb '{target_verb}' in sentence '{sentence}'\")\n", " target_doc = SPACY_MODEL(target_verb)\n", " target_tense = None\n", " for token in target_doc:\n", " if token.pos_ == \"VERB\":\n", " target_tense = token.tag_\n", " break\n", " print(f\"Debug: Target verb tense tag: {target_tense if target_tense else 'None'}\")\n", " tense_map = {\n", " 'VB': 'base form',\n", " 'VBD': 'past tense',\n", " 'VBG': 'gerund/present participle',\n", " 'VBN': 'past participle',\n", " 'VBP': 'present tense (non-3rd person singular)',\n", " 'VBZ': 'present tense (3rd person singular)'\n", " }\n", " tense_desc = tense_map.get(target_tense, 'base form') if target_tense else 'base form'\n", " print(f\"Debug: Tense description for prompt: {tense_desc}\")\n", " total_distractors_to_generate = 6\n", " prompt = (\n", " f\"Generate exactly {total_distractors_to_generate} English verbs that are distinct from '{target_verb}', \"\n", " f\"semantically related, and appropriate for CEFR level {level}. \"\n", " f\"The verbs must fit the context of the sentence '{sentence}' \"\n", " f\"and be in the {tense_desc} (e.g., '{target_verb}'). \"\n", " f\"Return only the verbs as a comma-separated list, with no additional text or non-verbs.\"\n", " )\n", " print(f\"Debug: LLM prompt:\\n{prompt}\")\n", " try:\n", " input_data = DISTRACTOR_TOKENIZER(\n", " prompt,\n", " return_tensors=\"pt\",\n", " padding=True,\n", " truncation=True,\n", " max_length=MAX_SEQ_LENGTH,\n", " ).to(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", " print(f\"Debug: Tokenized input data: {input_data}\")\n", " outputs = DISTRACTOR_MODEL.generate(\n", " input_ids=input_data[\"input_ids\"],\n", " attention_mask=input_data[\"attention_mask\"],\n", " max_new_tokens=50,\n", " pad_token_id=DISTRACTOR_TOKENIZER.pad_token_id,\n", " do_sample=True,\n", " temperature=0.6,\n", " top_k=50,\n", " top_p=0.85,\n", " )\n", " print(f\"Debug: Raw LLM output: {outputs}\")\n", " generated_text = DISTRACTOR_TOKENIZER.decode(outputs[0], skip_special_tokens=True)\n", " print(f\"Debug: Decoded LLM output: '{generated_text}'\")\n", " response_start = generated_text.find(\"<|start_header_id|>assistant<|end_header_id>\") + len(\"<|start_header_id|>assistant<|end_header_id>\")\n", " response_text = generated_text[response_start:].strip()\n", " print(f\"Debug: Extracted response text: '{response_text}'\")\n", " distractors = [v.strip() for v in response_text.split(\",\") if v.strip()]\n", " print(f\"Debug: Parsed distractors: {distractors}\")\n", " primary_distractors = []\n", " backup_distractors = []\n", " for verb in distractors:\n", " if not (len(verb.split()) == 1 and re.match(r'^[a-zA-Z]+$', verb)):\n", " print(f\"Debug: Invalid distractor '{verb}' - not a single alphabetic word\")\n", " continue\n", " if verb.lower() == target_verb.lower():\n", " print(f\"Debug: Invalid distractor '{verb}' - matches target verb\")\n", " continue\n", " doc = SPACY_MODEL(verb)\n", " is_verb = any(token.pos_ == \"VERB\" for token in doc)\n", " print(f\"Debug: Validating '{verb}' - is_verb: {is_verb}\")\n", " if not is_verb:\n", " print(f\"Debug: Invalid distractor '{verb}' - not recognized as a verb\")\n", " continue\n", " if len(primary_distractors) < num_distractors:\n", " primary_distractors.append(verb)\n", " elif len(backup_distractors) < num_distractors:\n", " backup_distractors.append(verb)\n", " if len(primary_distractors) >= num_distractors and len(backup_distractors) >= num_distractors:\n", " break\n", " print(f\"Debug: Primary distractors: {primary_distractors}\")\n", " print(f\"Debug: Backup distractors: {backup_distractors}\")\n", " selected_distractors = primary_distractors[:num_distractors]\n", " if len(selected_distractors) < num_distractors:\n", " print(f\"Debug: Insufficient primary distractors ({len(selected_distractors)}/{num_distractors}). Using backups.\")\n", " needed = num_distractors - len(selected_distractors)\n", " valid_backups = [v for v in backup_distractors if v not in selected_distractors]\n", " selected_distractors.extend(valid_backups[:needed])\n", " print(f\"Debug: Added backups: {valid_backups[:needed]}\")\n", " if len(selected_distractors) < num_distractors:\n", " print(f\"Debug: Insufficient distractors after backups ({len(selected_distractors)}/{num_distractors}). Using fallbacks.\")\n", " fallback_verbs = [\"walk\", \"talk\", \"look\", \"move\", \"say\"]\n", " fallback_verbs = [v for v in fallback_verbs if v.lower() != target_verb.lower() and v not in selected_distractors]\n", " selected_distractors.extend(fallback_verbs[:num_distractors - len(selected_distractors)])\n", " print(f\"Debug: Added fallbacks: {selected_distractors[len(selected_distractors) - (num_distractors - len(selected_distractors)):]}\")\n", " print(f\"Debug: Final distractors: {selected_distractors[:num_distractors]}\")\n", " return selected_distractors[:num_distractors]\n", " except Exception as e:\n", " print(f\"Debug: Error generating distractors with LLM: {str(e)}\")\n", " print(f\"Debug: Using fallback distractors due to error\")\n", " fallback_verbs = [\"walk\", \"talk\", \"look\"]\n", " return fallback_verbs[:num_distractors]\n", "\n", " def create_multiple_choice(sentence: str, level: str) -> Optional[Dict]:\n", " doc = SPACY_MODEL(sentence)\n", " verb = None\n", " verb_index = None\n", " for i, token in enumerate(doc):\n", " if token.pos_ in [\"VERB\", \"AUX\"] and token.dep_ in [\"ROOT\", \"cop\"]:\n", " if token.text.lower() not in [\"to\"]:\n", " verb = token.text\n", " verb_index = i\n", " break\n", " if not verb:\n", " print(\"Debug: No valid verb for multiple choice, skipping.\")\n", " error_counts[\"invalid_multiple_choice\"] += 1\n", " return None\n", " words = [token.text for token in doc]\n", " words[verb_index] = \"__\"\n", " blanked_sentence = \" \".join(words).strip()\n", " distractors = generate_contextual_distractors(verb, sentence)\n", " options = distractors + [verb]\n", " cleaned_options = []\n", " for opt in options:\n", " clean_opt = re.sub(r'[^a-zA-Z]', '', opt.lower()).strip()\n", " if clean_opt and len(clean_opt.split()) == 1:\n", " cleaned_options.append(clean_opt)\n", " if len(cleaned_options) < 4:\n", " print(\"Debug: Not enough valid options, returning None\")\n", " error_counts[\"invalid_multiple_choice\"] += 1\n", " return None\n", " random.shuffle(cleaned_options)\n", " return {\n", " \"question_text\": blanked_sentence,\n", " \"options\": cleaned_options,\n", " \"correct_answer\": verb.lower()\n", " }\n", "\n", " def create_arrange_question(sentence: str, level: str) -> Optional[Dict]:\n", " doc = SPACY_MODEL(sentence)\n", " words = [token.text for token in doc if token.text != '.']\n", " if len(words) < 3:\n", " print(\"Debug: Sentence too short for arrange question, skipping.\")\n", " error_counts[\"invalid_arrange\"] += 1\n", " return None\n", " random.shuffle(words)\n", " if ' '.join(words).strip().lower() == sentence.strip().lower():\n", " random.shuffle(words)\n", " return {\n", " \"question_text\": \"Arrange the words to form the correct sentence.\",\n", " \"words\": words,\n", " \"correct_sentence\": sentence\n", " }\n", "\n", " def create_labeling_question(sentence: str, level: str) -> Optional[Dict]:\n", " cleaned_sentence = clean_sentence(sentence)\n", " if cleaned_sentence is None:\n", " print(\"Debug: Invalid sentence for labeling question, skipping.\")\n", " return None\n", " doc = SPACY_MODEL(cleaned_sentence)\n", " root_count = sum(1 for token in doc if token.dep_ == \"ROOT\")\n", " if root_count > 1:\n", " print(\"Debug: Multiple clauses detected, skipping labeling question.\")\n", " error_counts[\"multiple_clauses\"] += 1\n", " return None\n", " is_passive = any(token.dep_ == \"auxpass\" for token in doc)\n", " if is_passive:\n", " print(\"Debug: Passive voice detected, skipping labeling question.\")\n", " error_counts[\"passive_voice\"] += 1\n", " return None\n", " constituents = {\n", " \"subject\": None,\n", " \"verb\": None,\n", " \"object\": None,\n", " \"adjective\": None,\n", " \"adverb\": None,\n", " \"prepositional phrase\": None\n", " }\n", " prep_obj = None\n", " main_verb = None\n", " for token in doc:\n", " if token.dep_ == \"nsubj\" and not constituents[\"subject\"]:\n", " constituents[\"subject\"] = token.text\n", " for child in token.children:\n", " if child.dep_ in [\"det\", \"compound\"]:\n", " constituents[\"subject\"] = f\"{child.text} {constituents['subject']}\"\n", " if token.dep_ == \"ROOT\" and token.pos_ in [\"VERB\", \"AUX\"]:\n", " if token.pos_ == \"AUX\" and not main_verb:\n", " for child in token.children:\n", " if child.pos_ == \"VERB\" and child.dep_ in [\"xcomp\", \"ccomp\", \"advcl\"]:\n", " main_verb = child.text\n", " break\n", " if not main_verb:\n", " main_verb = token.text\n", " if token.dep_ == \"dobj\" and not constituents[\"object\"]:\n", " obj = token.text\n", " for child in token.children:\n", " if child.dep_ in [\"det\", \"amod\", \"compound\"]:\n", " obj = f\"{child.text} {obj}\"\n", " constituents[\"object\"] = obj\n", " if token.dep_ in [\"amod\", \"acomp\"] and token.pos_ == \"ADJ\" and not constituents[\"adjective\"]:\n", " constituents[\"adjective\"] = token.text\n", " if token.dep_ == \"advmod\" and token.pos_ == \"ADV\" and not constituents[\"prepositional phrase\"]:\n", " for child in token.head.children:\n", " if child.dep_ == \"prep\" and child.text in [\"around\", \"about\"]:\n", " constituents[\"prepositional phrase\"] = f\"{child.text}\"\n", " for gc in child.children:\n", " if gc.dep_ == \"pobj\":\n", " constituents[\"prepositional phrase\"] += f\" {gc.text}\"\n", " break\n", " print(f\"Debug: Relabeled '{token.text}' as prepositional phrase\")\n", " break\n", " else:\n", " constituents[\"adverb\"] = token.text\n", " if token.dep_ == \"prep\" and not constituents[\"prepositional phrase\"]:\n", " for child in token.children:\n", " if child.dep_ == \"pobj\":\n", " prep_obj = child.text\n", " modifiers = []\n", " for grandchild in child.children:\n", " if grandchild.dep_ in [\"det\", \"amod\", \"nummod\", \"compound\"]:\n", " modifiers.append(grandchild.text)\n", " modifiers = sorted(modifiers, key=lambda x: doc.text.find(x))\n", " prep_obj = \" \".join(modifiers + [prep_obj]).strip()\n", " constituents[\"prepositional phrase\"] = f\"{token.text} {prep_obj}\"\n", " break\n", " constituents[\"verb\"] = main_verb\n", " if constituents[\"verb\"]:\n", " for contraction, expansion in CONTRACTION_MAP.items():\n", " if contraction in constituents[\"verb\"].lower():\n", " constituents[\"verb\"] = expansion\n", " valid_constituents = [k for k, v in constituents.items() if v is not None]\n", " if len(valid_constituents) < 3:\n", " print(f\"Debug: Insufficient constituents ({len(valid_constituents)}/3): \" +\n", " \", \".join(f\"{k} ({v if v else 'None'})\" for k, v in constituents.items()) +\n", " \", skipping labeling question.\")\n", " error_counts[\"insufficient_constituents\"] += 1\n", " return None\n", " valid_values = [v.lower() for v in constituents.values() if v is not None]\n", " if len(set(valid_values)) != len(valid_values):\n", " print(\"Debug: Constituents are not distinct, skipping labeling question.\")\n", " error_counts[\"no_valid_constituents\"] += 1\n", " return None\n", " correct_labels = {k: v for k, v in constituents.items() if v is not None}\n", " return {\n", " \"question_text\": cleaned_sentence,\n", " \"instruction\": f\"Assign {', '.join(correct_labels.keys())} to the correct words or phrases.\",\n", " \"correct_labels\": correct_labels\n", " }\n", "\n", " validated_sentences = []\n", " target_score = cefr_to_score.get(level, 1)\n", " attempt_count = 0\n", " max_attempts = 200\n", "\n", " while len(validated_sentences) < num_sentences and attempt_count < max_attempts:\n", " attempt_count += 1\n", " print(f\"Debug: Generating sentence attempt {attempt_count}/{max_attempts} for level {level}\")\n", " try:\n", " input_text = f\"### Input:\\nGenerate a sentence at CEFR level {level}\"\n", " if topic:\n", " input_text += f\" about {topic}\"\n", " input_text += \" Please don't use to be Verb and just output the sentence\\n### Response:\\n\"\n", " input_data = GENERATOR_TOKENIZER(\n", " input_text,\n", " return_tensors=\"pt\",\n", " padding=True,\n", " truncation=True,\n", " max_length=MAX_SEQ_LENGTH,\n", " ).to(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", " max_tokens_map = {\n", " 'A1': 30,\n", " 'A2': 30,\n", " 'B1': 35,\n", " 'B2': 35,\n", " 'C1': 50,\n", " 'C2': 60\n", " }\n", " max_new_tokens = max_tokens_map.get(level, 30)\n", " print(f\"Debug: Using max_new_tokens={max_new_tokens} for level {level}\")\n", " outputs = GENERATOR_MODEL.generate(\n", " input_ids=input_data[\"input_ids\"],\n", " attention_mask=input_data[\"attention_mask\"],\n", " max_new_tokens=max_new_tokens,\n", " pad_token_id=GENERATOR_TOKENIZER.pad_token_id,\n", " do_sample=True,\n", " temperature=0.8,\n", " top_k=60,\n", " top_p=0.9,\n", " )\n", " if not isinstance(outputs, torch.Tensor) or outputs.numel() == 0:\n", " print(\"Debug: Invalid model output, skipping.\")\n", " continue\n", " generated_text = GENERATOR_TOKENIZER.decode(outputs[0], skip_special_tokens=True)\n", " if not isinstance(generated_text, str) or not generated_text.strip():\n", " print(\"Debug: Invalid decoded text, skipping.\")\n", " continue\n", " response_start = generated_text.find(\"### Response:\\n\") + len(\"### Response:\\n\")\n", " response_text = generated_text[response_start:].strip()\n", " response_text = re.sub(r'^Example sentence at level [A-C][1-2]: ', '', response_text, flags=re.IGNORECASE)\n", " original_response = response_text\n", " print(f\"Debug: Raw model output: '{original_response}'\")\n", " response_text = clean_sentence(response_text)\n", " if response_text is None:\n", " error_counts[\"no_full_stop\"] += 1\n", " print(\"Debug: Sentence rejected due to missing full-stop, retrying.\")\n", " continue\n", " print(f\"Debug: Original sentence: '{original_response}'\")\n", " print(f\"Debug: Cleaned sentence: '{response_text}'\")\n", " if original_response != response_text:\n", " print(f\"Debug: Processed sentence: '{original_response}' -> '{response_text}'\")\n", " error_counts[\"output_pattern_removed\"] += 1\n", " for contraction, expansion in CONTRACTION_MAP.items():\n", " response_text = re.sub(rf'\\b(\\w+){contraction}\\b', r'\\1 ' + expansion, response_text, flags=re.IGNORECASE)\n", " doc = SPACY_MODEL(response_text)\n", " sents = list(doc.sents)\n", " first_sent = sents[0].text.strip() if sents else response_text\n", " if len(first_sent.split()) < 3:\n", " print(\"Debug: Sentence too short, skipping.\")\n", " continue\n", " doc = SPACY_MODEL(first_sent)\n", " verbs = []\n", " for token in doc:\n", " if token.pos_ in [\"VERB\", \"AUX\"] and token.dep_ in [\"ROOT\", \"cop\", \"aux\"]:\n", " if token.text.lower() == \"to\" and token.dep_ == \"aux\":\n", " continue\n", " if token.pos_ == \"AUX\" and token.dep_ == \"ROOT\":\n", " for child in token.children:\n", " if child.pos_ == \"VERB\" and child.dep_ in [\"xcomp\", \"ccomp\", \"advcl\"]:\n", " verbs.append(child.text)\n", " print(f\"Debug: Selected main verb '{child.text}' over modal '{token.text}'\")\n", " break\n", " else:\n", " verbs.append(token.text)\n", " else:\n", " verbs.append(token.text)\n", " if not verbs and \"'\" in first_sent:\n", " contractions = [token for token in doc if \"'\" in token.text and token.pos_ in [\"VERB\", \"AUX\"]]\n", " if contractions:\n", " verbs.append(contractions[0].text)\n", " verb = verbs[0] if verbs else None\n", " if not verb:\n", " error_counts[\"no_valid_verb\"] += 1\n", " print(\"Debug: No valid verb found, skipping.\")\n", " continue\n", " predicted_score = predict_sentiment(first_sent)\n", " if abs(predicted_score - target_score) > 1:\n", " error_counts[\"cefr_mismatch\"] += 1\n", " print(\"Debug: Sentence CEFR level mismatch, skipping.\")\n", " continue\n", " if any(existing[\"sentence\"] == first_sent for existing in validated_sentences):\n", " error_counts[\"duplicate_sentence\"] += 1\n", " print(\"Debug: Duplicate sentence, skipping.\")\n", " continue\n", " sentence_data = {\n", " \"sentence\": first_sent,\n", " \"verb\": verb,\n", " \"level\": level,\n", " \"for_user\": user_id\n", " }\n", " if \"selection\" in question_types:\n", " selection_question = create_selection_question(first_sent, level)\n", " sentence_data[\"selection_question\"] = selection_question\n", " if \"labeling\" in question_types:\n", " labeling_question = create_labeling_question(first_sent, level)\n", " sentence_data[\"labeling_question\"] = labeling_question\n", " if \"fill_in_blank\" in question_types:\n", " fill_in_blank_question = create_fill_in_blank(first_sent, level)\n", " sentence_data[\"fill_in_blank_question\"] = fill_in_blank_question\n", " if \"multiple_choice\" in question_types:\n", " multiple_choice_question = create_multiple_choice(first_sent, level)\n", " sentence_data[\"multiple_choice_question\"] = multiple_choice_question\n", " if \"arrange\" in question_types:\n", " arrange_question = create_arrange_question(first_sent, level)\n", " sentence_data[\"arrange_question\"] = arrange_question\n", " if not any([sentence_data.get(qt + \"_question\") for qt in question_types]):\n", " print(\"Debug: No valid questions generated, skipping.\")\n", " continue\n", " validated_sentences.append(sentence_data)\n", " print(f\"Debug: Added valid sentence: '{first_sent}'\")\n", " except Exception as e:\n", " print(f\"Error generating sentence: {str(e)}\")\n", " continue\n", "\n", " if not validated_sentences:\n", " print(f\"Warning: No valid sentences generated after {attempt_count} attempts.\")\n", "\n", " print(\"\\nError Statistics:\")\n", " print(f\"Duplicate sentence = {error_counts['duplicate_sentence']} Case\")\n", " print(f\"Sentence CEFR level mismatch = {error_counts['cefr_mismatch']} Case\")\n", " print(f\"No valid verb found = {error_counts['no_valid_verb']} Case\")\n", " print(f\"No valid constituents = {error_counts['no_valid_constituents']} Case\")\n", " print(f\"Insufficient constituents = {error_counts['insufficient_constituents']} Case\")\n", " print(f\"Multiple clauses = {error_counts['multiple_clauses']} Case\")\n", " print(f\"Passive voice = {error_counts['passive_voice']} Case\")\n", " print(f\"Duplicate selection sentences = {error_counts['duplicate_selection']} Case\")\n", " print(f\"Invalid incorrect sentences = {error_counts['invalid_incorrect_sentence']} Case\")\n", " print(f\"Invalid fill-in-the-blank = {error_counts['invalid_fill_in_blank']} Case\")\n", " print(f\"Invalid multiple choice = {error_counts['invalid_multiple_choice']} Case\")\n", " print(f\"Invalid arrange questions = {error_counts['invalid_arrange']} Case\")\n", " print(f\"No full-stop in sentence = {error_counts['no_full_stop']} Case\")\n", "\n", " return validated_sentences\n", "\n", "# Evaluation functions\n", "def evaluate_selection(selected: str, correct: str) -> Dict:\n", " selected = clean_sentence(selected)\n", " if selected is None:\n", " return {\n", " \"is_correct\": False,\n", " \"score\": 0.0,\n", " \"message\": \"Invalid selected sentence: no full-stop found.\"\n", " }\n", " correct = clean_sentence(correct)\n", " if correct is None:\n", " return {\n", " \"is_correct\": False,\n", " \"score\": 0.0,\n", " \"message\": \"Invalid correct sentence: no full-stop found.\"\n", " }\n", " is_correct = selected.strip() == correct.strip()\n", " score = 1.0 if is_correct else 0.0\n", " return {\n", " \"is_correct\": is_correct,\n", " \"score\": score,\n", " \"message\": \"Selection is correct.\" if is_correct else \"Selection is incorrect.\"\n", " }\n", "\n", "def evaluate_labeling(sentence: str, user_labels: Dict[str, str]) -> Dict:\n", " global SPACY_MODEL\n", " cleaned_sentence = clean_sentence(sentence)\n", " if cleaned_sentence is None:\n", " return {\n", " \"is_correct\": False,\n", " \"score\": 0.0,\n", " \"message\": \"Invalid sentence: no full-stop found.\"\n", " }\n", " doc = SPACY_MODEL(cleaned_sentence)\n", " correct_labels = {\n", " \"subject\": None,\n", " \"verb\": None,\n", " \"object\": None,\n", " \"adjective\": None,\n", " \"adverb\": None,\n", " \"prepositional phrase\": None\n", " }\n", " prep_obj = None\n", " main_verb = None\n", " for token in doc:\n", " if token.dep_ == \"nsubj\" and not correct_labels[\"subject\"]:\n", " correct_labels[\"subject\"] = token.text\n", " for child in token.children:\n", " if child.dep_ in [\"det\", \"compound\"]:\n", " correct_labels[\"subject\"] = f\"{child.text} {correct_labels['subject']}\"\n", " if token.dep_ == \"ROOT\" and token.pos_ in [\"VERB\", \"AUX\"]:\n", " if token.pos_ == \"AUX\" and not main_verb:\n", " for child in token.children:\n", " if child.pos_ == \"VERB\" and child.dep_ in [\"xcomp\", \"ccomp\", \"advcl\"]:\n", " main_verb = child.text\n", " break\n", " if not main_verb:\n", " main_verb = token.text\n", " if token.dep_ == \"dobj\" and not correct_labels[\"object\"]:\n", " obj = token.text\n", " for child in token.children:\n", " if child.dep_ in [\"det\", \"amod\", \"compound\"]:\n", " obj = f\"{child.text} {obj}\"\n", " correct_labels[\"object\"] = obj\n", " if token.dep_ in [\"amod\", \"acomp\"] and token.pos_ == \"ADJ\" and not correct_labels[\"adjective\"]:\n", " correct_labels[\"adjective\"] = token.text\n", " if token.dep_ == \"advmod\" and token.pos_ == \"ADV\" and not correct_labels[\"prepositional phrase\"]:\n", " for child in token.head.children:\n", " if child.dep_ == \"prep\" and child.text in [\"around\", \"about\"]:\n", " correct_labels[\"prepositional phrase\"] = f\"{child.text}\"\n", " for gc in child.children:\n", " if gc.dep_ == \"pobj\":\n", " correct_labels[\"prepositional phrase\"] += f\" {gc.text}\"\n", " break\n", " print(f\"Debug: Relabeled '{token.text}' as prepositional phrase\")\n", " break\n", " else:\n", " correct_labels[\"adverb\"] = token.text\n", " if token.dep_ == \"prep\" and not correct_labels[\"prepositional phrase\"]:\n", " for child in token.children:\n", " if child.dep_ == \"pobj\":\n", " prep_obj = child.text\n", " for grandchild in child.children:\n", " if grandchild.dep_ in [\"det\", \"amod\"]:\n", " prep_obj = f\"{grandchild.text} {prep_obj}\"\n", " correct_labels[\"prepositional phrase\"] = f\"{token.text} {prep_obj}\"\n", " break\n", " correct_labels[\"verb\"] = main_verb\n", " if correct_labels[\"verb\"]:\n", " for contraction, expansion in {\n", " \"'ve\": \"have\",\n", " \"'s\": \"is\",\n", " \"'re\": \"are\",\n", " \"'m\": \"am\",\n", " \"'ll\": \"will\",\n", " \"'d\": \"had\"\n", " }.items():\n", " if contraction in correct_labels[\"verb\"].lower():\n", " correct_labels[\"verb\"] = expansion\n", " valid_correct_labels = {k: v for k, v in correct_labels.items() if v is not None}\n", " if not valid_correct_labels:\n", " return {\n", " \"is_correct\": False,\n", " \"score\": 0.0,\n", " \"message\": \"No valid constituents found in sentence.\"\n", " }\n", " score_per_label = 1.0 / len(valid_correct_labels)\n", " is_correct = all(\n", " user_labels.get(key, \"\").strip().lower() == value.strip().lower()\n", " for key, value in valid_correct_labels.items()\n", " )\n", " score = sum(\n", " score_per_label\n", " for key, value in valid_correct_labels.items()\n", " if user_labels.get(key, \"\").strip().lower() == value.strip().lower()\n", " )\n", " return {\n", " \"is_correct\": is_correct,\n", " \"score\": round(score, 2),\n", " \"message\": \"Labeling is correct.\" if is_correct else \"Labeling is incorrect.\"\n", " }\n", "\n", "def evaluate_fill_in_blank(user_answer: str, correct_answer: str) -> Dict:\n", " is_correct = user_answer.strip().lower() == correct_answer.strip().lower()\n", " score = 1.0 if is_correct else 0.0\n", " return {\n", " \"is_correct\": is_correct,\n", " \"score\": score,\n", " \"message\": \"Answer is correct.\" if is_correct else \"Answer is incorrect.\"\n", " }\n", "\n", "def evaluate_multiple_choice(selected_option: str, correct_answer: str) -> Dict:\n", " is_correct = selected_option.strip().lower() == correct_answer.strip().lower()\n", " score = 1.0 if is_correct else 0.0\n", " return {\n", " \"is_correct\": is_correct,\n", " \"score\": score,\n", " \"message\": \"Selection is correct.\" if is_correct else \"Selection is incorrect.\"\n", " }\n", "\n", "def evaluate_arrange(user_arrangement: List[str], correct_sentence: str) -> Dict:\n", " user_sentence = \" \".join(user_arrangement).strip()\n", " correct_sentence = clean_sentence(correct_sentence)\n", " if correct_sentence is None:\n", " return {\n", " \"is_correct\": False,\n", " \"score\": 0.0,\n", " \"message\": \"Invalid correct sentence: no full-stop found.\"\n", " }\n", " is_correct = user_sentence.lower() == correct_sentence.lower()\n", " score = 1.0 if is_correct else 0.0\n", " return {\n", " \"is_correct\": is_correct,\n", " \"score\": score,\n", " \"message\": \"Arrangement is correct.\" if is_correct else \"Arrangement is incorrect.\"\n", " }\n", "\n", "# API endpoints\n", "@app.post(\"/generate_sentences\", response_model=SentenceResponse)\n", "async def generate_sentences(request: Request, sentence_request: SentenceRequest):\n", " start_time = time.time()\n", " try:\n", " level = sentence_request.level\n", " num_sentences = sentence_request.num_sentences\n", " user_id = sentence_request.user_id\n", " topic = sentence_request.topic\n", " question_types = sentence_request.question_types\n", " valid_question_types = [\"selection\", \"labeling\", \"fill_in_blank\", \"multiple_choice\", \"arrange\"]\n", " if not all(qt in valid_question_types for qt in question_types):\n", " raise HTTPException(status_code=400, detail=f\"Invalid question types. Must be subset of: {valid_question_types}\")\n", " if level not in [\"A1\", \"A2\", \"B1\", \"B2\", \"C1\", \"C2\"]:\n", " raise HTTPException(status_code=400, detail=\"Invalid level. Must be one of: A1, A2, B1, B2, C1, C2\")\n", " if num_sentences < 1 or num_sentences > 10:\n", " raise HTTPException(status_code=400, detail=\"Number of sentences must be between 1 and 10\")\n", " sentences = generate_sentences_internal(level, num_sentences, user_id, topic, question_types)\n", " processing_time = time.time() - start_time\n", " current_time = datetime.datetime.now().isoformat()\n", " if not sentences:\n", " print(\"Debug: No sentences generated, returning empty response\")\n", " return {\n", " \"success\": False,\n", " \"sentences\": [],\n", " \"message\": \"Failed to generate sentences. Please try again.\",\n", " \"processing_time\": processing_time,\n", " \"timestamp\": current_time\n", " }\n", " response = {\n", " \"success\": True,\n", " \"sentences\": sentences,\n", " \"message\": f\"Generated {len(sentences)} sentences for level {level}\" +\n", " (f\" on topic '{topic}'\" if topic else \"\") +\n", " f\" with question types {question_types}\",\n", " \"processing_time\": processing_time,\n", " \"timestamp\": current_time\n", " }\n", " print(f\"Debug: Sending response: {response}\")\n", " return response\n", " except HTTPException:\n", " raise\n", " except Exception as e:\n", " processing_time = time.time() - start_time\n", " current_time = datetime.datetime.now().isoformat()\n", " print(f\"API error: {str(e)}\")\n", " return {\n", " \"success\": False,\n", " \"sentences\": [],\n", " \"message\": f\"Internal server error: {str(e)}\",\n", " \"processing_time\": processing_time,\n", " \"timestamp\": current_time\n", " }\n", "\n", "@app.post(\"/evaluate_selection\", response_model=SelectionResponse)\n", "async def evaluate_selection(request: Request, selection_request: SelectionRequest):\n", " start_time = time.time()\n", " try:\n", " result = evaluate_selection(\n", " selection_request.selected_sentence,\n", " selection_request.correct_sentence\n", " )\n", " processing_time = time.time() - start_time\n", " current_time = datetime.datetime.now().isoformat()\n", " return {\n", " \"success\": True,\n", " \"is_correct\": result[\"is_correct\"],\n", " \"score\": result[\"score\"],\n", " \"message\": result[\"message\"],\n", " \"processing_time\": processing_time,\n", " \"timestamp\": current_time\n", " }\n", " except Exception as e:\n", " processing_time = time.time() - start_time\n", " current_time = datetime.datetime.now().isoformat()\n", " return {\n", " \"success\": False,\n", " \"is_correct\": False,\n", " \"score\": 0.0,\n", " \"message\": f\"Error evaluating selection: {str(e)}\",\n", " \"processing_time\": processing_time,\n", " \"timestamp\": current_time\n", " }\n", "\n", "def run_server(port: int = 8000, max_attempts: int = 5):\n", " import socket\n", " for attempt in range(max_attempts):\n", " try:\n", " with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n", " s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n", " s.bind((\"0.0.0.0\", port))\n", " config = uvicorn.Config(\n", " app,\n", " host=\"0.0.0.0\",\n", " port=port,\n", " log_level=\"info\",\n", " timeout_keep_alive=30\n", " )\n", " server = uvicorn.Server(config)\n", " print(f\"Starting server on port {port}...\")\n", " asyncio.run(server.serve())\n", " break\n", " except OSError as e:\n", " if \"address already in use\" in str(e).lower() and attempt < max_attempts - 1:\n", " print(f\"Port {port} in use, trying port {port + 1}...\")\n", " port += 1\n", " time.sleep(1)\n", " else:\n", " print(f\"Failed to start server: {str(e)}\")\n", " raise\n", " except Exception as e:\n", " print(f\"Server error: {str(e)}\")\n", " raise\n", "\n", "def start_ngrok(port: int) -> Optional[str]:\n", " try:\n", " ngrok.kill()\n", " ngrok.set_auth_token(\"2oFl98p58vTuHbdQu5m2t1exmDW_5MXsrmeuZtv9J5hTscGzb\")\n", " public_url = ngrok.connect(port, bind_tls=True).public_url\n", " print(f\"Ngrok tunnel created: {public_url}\")\n", " return public_url\n", " except Exception as e:\n", " print(f\"Ngrok error: {str(e)}\")\n", " print(\"Continuing without ngrok - you'll need to access the API locally\")\n", " return None\n", "\n", "def start_application():\n", " try:\n", " load_models()\n", " except Exception as e:\n", " print(f\"Failed to load models: {str(e)}\")\n", " return\n", " port = 8000\n", " max_attempts = 5\n", " attempt = 0\n", " while attempt < max_attempts:\n", " try:\n", " server_thread = threading.Thread(target=run_server, args=(port,))\n", " server_thread.daemon = True\n", " server_thread.start()\n", " time.sleep(2)\n", " public_url = start_ngrok(port)\n", " if public_url:\n", " print(f\"\\nAPI is available at: {public_url}/generate_sentences\")\n", " else:\n", " print(f\"\\nAPI is running locally at: http://localhost:{port}/generate_sentences\")\n", " while True:\n", " time.sleep(1)\n", " except KeyboardInterrupt:\n", " print(\"\\nShutting down server...\")\n", " ngrok.kill()\n", " import os\n", " os._exit(0)\n", " except Exception as e:\n", " print(f\"Application error: {str(e)}\")\n", " ngrok.kill()\n", " attempt += 1\n", " port += 1\n", " time.sleep(2)\n", " if attempt < max_attempts:\n", " print(f\"Retrying with port {port}...\")\n", "\n", "if __name__ == \"__main__\":\n", " start_application()" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", 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