import openai
import gradio as gr

# Import all data modules
from company_profile import company_profile
from financials import financials
from workforce import workforce
# Import other modules as needed

# Set up the OpenAI (GROQ) API
openai.api_key = "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"
openai.api_base = "https://api.groq.com/openai/v1"

# Function to query the AI agent
def query_ai(user_input):
    # Contextual data from the application
    context_data = f"""
    Company Name: {company_profile['brand_name']}
    Headquarters: {company_profile['headquarters']}
    Founders: {', '.join(company_profile['founders'])}
    Latest Valuation: {financials['latest_valuation']}
    Total Funding: {financials['total_funding']}
    Employees: {workforce['total_employees']}
    """

    # Call the OpenAI API
    try:
        response = openai.ChatCompletion.create(
            model="llama-3.1-70b-versatile",
            messages=[
                {"role": "system", "content": "You are a Private Market Analysis AI Agent."},
                {"role": "user", "content": f"{context_data}\n\n{user_input}"}
            ]
        )
        return response.choices[0].message["content"]
    except Exception as e:
        return f"Error: {str(e)}"

# Gradio interface
def chatbot_interface(user_input):
    return query_ai(user_input)

# Launch the Gradio app
gr.Interface(
    fn=chatbot_interface,
    inputs="text",
    outputs="text",
    title="Satyam Market Analysis",
    description="Ask me about private companies like Razorpay, including financials, products, and more!"
).launch()