koko210Serve
d58be3b33e
- Remove Ollama-specific files (Dockerfile.ollama, entrypoint.sh) - Replace all query_ollama imports and calls with query_llama - Remove langchain-ollama dependency from requirements.txt - Update all utility files (autonomous, kindness, image_generation, etc.) - Update README.md documentation references - Maintain backward compatibility alias in llm.py
44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
from utils.llm import query_llama
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async def analyze_sentiment(messages: list) -> tuple[str, float]:
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"""
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Analyze the sentiment of a conversation using llama.cpp
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Returns a tuple of (sentiment description, positivity score from 0-1)
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"""
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# Combine the last few messages for context (up to 5)
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messages_to_analyze = messages[-5:] if len(messages) > 5 else messages
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conversation_text = "\n".join([
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f"{'Bot' if msg['is_bot_message'] else 'User'}: {msg['content']}"
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for msg in messages_to_analyze
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])
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prompt = f"""Analyze the sentiment and tone of this conversation snippet between a user and a bot.
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Focus on the overall mood, engagement level, and whether the interaction seems positive/neutral/negative.
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Give a brief 1-2 sentence summary and a positivity score from 0-1 where:
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0.0-0.3 = Negative/Hostile
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0.3-0.7 = Neutral/Mixed
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0.7-1.0 = Positive/Friendly
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Conversation:
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{conversation_text}
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Format your response exactly like this example:
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Summary: The conversation is friendly and engaging with good back-and-forth.
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Score: 0.85
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Response:"""
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try:
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response = await query_llama(prompt)
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if not response or 'Score:' not in response:
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return "Could not analyze sentiment", 0.5
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# Parse the response
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lines = response.strip().split('\n')
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summary = lines[0].replace('Summary:', '').strip()
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score = float(lines[1].replace('Score:', '').strip())
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return summary, score
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except Exception as e:
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print(f"Error in sentiment analysis: {e}")
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return "Error analyzing sentiment", 0.5 |