Update app.py

app.py

@@ -21,8 +21,8 @@ task_instructions = {
 }
 
 
-# Enhanced text preprocessing function
-def preprocess_text(text):
+# Enhanced text preprocessing function (from pipeline)
+def preprocess_input(text):
     """
     Clean and validate the user's input text with better error handling and language detection.
     """
@@ -39,16 +39,46 @@ def preprocess_text(text):
     return text.strip()
 
 
-# Enhanced respond function with better error handling and structured flow
+# Model inference function (from pipeline)
+def run_model_inference(messages, max_tokens, temperature, top_p):
+    """
+    Run model inference based on the messages with specified parameters.
+    """
+    try:
+        response = ""
+        for chunk in client.chat_completion(
+            messages=messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = chunk.choices[0].delta.content
+            response += token
+            yield response
+    except Exception as e:
+        yield f"Error generating response: {str(e)}"
+
+
+# Output postprocessing (from pipeline)
+def postprocess_output(response):
+    """
+    Postprocess the model's response before presenting it to the user.
+    """
+    # Example: Clean up the response or format it if necessary
+    return response.strip()
+
+
+# Enhanced respond function with pipeline integration
 def respond(task, message, history, system_message, max_tokens, temperature, top_p):
     """
-    Handle user messages and generate responses using the NLP model with improved error handling and response flow.
+    Handle user messages and generate responses using the NLP model with integrated pipeline.
     """
     # Apply task-specific instructions
     system_message = f"{system_message} Task: {task_instructions.get(task, 'General NLP task')}"
 
-    # Preprocess the user's input
-    message = preprocess_text(message)
+    # Preprocess the user's input using the pipeline
+    message = preprocess_input(message)
     if message.startswith("Input language detected") or message.startswith("Unable to detect"):
         return message  # Early exit on language issues
 
@@ -62,21 +92,18 @@ def respond(task, message, history, system_message, max_tokens, temperature, top
 
     messages.append({"role": "user", "content": message})
 
-    # Stream response from the Hugging Face model with improved error handling
-    try:
-        response = ""
-        for chunk in client.chat_completion(
-            messages=messages,
-            max_tokens=max_tokens,
-            stream=True,
-            temperature=temperature,
-            top_p=top_p,
-        ):
-            token = chunk.choices[0].delta.content
-            response += token
-            yield response
-    except Exception as e:
-        yield f"Error generating response: {str(e)}"
+    # Get model response using the pipeline function (streamed)
+    response = ""
+    for chunk in run_model_inference(
+        messages=messages,
+        max_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p
+    ):
+        response = chunk
+
+    # Postprocess the model's response before sending it to the user
+    return postprocess_output(response)
 
 
 # Improved chat history management functions with better file handling