ef749e4878
- Set up FastAPI application structure - Implement SQLite database with SQLAlchemy - Create WhatsApp webhook endpoints - Implement message storage and analysis - Integrate Gemini 2.5 Pro for message analysis - Add email delivery of insights - Configure APScheduler for weekend analysis - Add linting with Ruff
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
"""
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Service for interacting with Google's Gemini API.
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"""
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import logging
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import google.generativeai as genai
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from app.core.config import settings
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Configure Gemini API
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genai.configure(api_key=settings.GEMINI_API_KEY)
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async def analyze_messages(messages: list[str]) -> str | None:
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"""
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Analyze a list of WhatsApp messages using Gemini 2.5 Pro.
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Args:
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messages: List of message texts to analyze
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Returns:
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Analysis text or None if analysis failed
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"""
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try:
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# Join messages into a single string with line breaks
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messages_text = "\n\n".join(messages)
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# Prepare prompt for Gemini
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prompt = f"""
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You are an expert in analyzing WhatsApp conversations. I will provide you with a set of WhatsApp messages.
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Please analyze these messages and provide the following insights:
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1. Key topics and themes discussed
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2. Sentiment analysis (overall mood of the conversations)
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3. Frequent participants and their engagement levels
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4. Any action items or follow-ups mentioned
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5. Suggestions or recommendations based on the conversations
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Format your response in a well-organized manner with clear headings and bullet points.
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Here are the messages to analyze:
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{messages_text}
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"""
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# Get Gemini model
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model = genai.GenerativeModel(model_name="gemini-1.5-pro")
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# Generate response
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response = await model.generate_content_async(prompt)
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# Return the analysis text
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return response.text
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except Exception as e:
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logger.exception(f"Error analyzing messages with Gemini: {str(e)}")
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return None
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