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https://github.com/bnair123/MusicAnalyser.git
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f4432154b6
- Refactor Database: Add `Artist` model, M2M relationship, and `AnalysisSnapshot` model. - Backend Services: Implement `StatsService` for computable metrics and `NarrativeService` for Gemini LLM integration. - Fix Ingestion: Correctly handle multiple artists per track and backfill existing data. - Testing: Add unit tests for statistics logic and live verification scripts. - Documentation: Add `PHASE_4_FRONTEND_GUIDE.md`.
68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
import os
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import json
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import google.generativeai as genai
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from typing import Dict, Any
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class NarrativeService:
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def __init__(self, model_name: str = "gemini-2.5-flash"):
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self.api_key = os.getenv("GEMINI_API_KEY")
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if not self.api_key:
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print("WARNING: GEMINI_API_KEY not found. LLM features will fail.")
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else:
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genai.configure(api_key=self.api_key)
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self.model_name = model_name
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def generate_narrative(self, stats_json: Dict[str, Any]) -> Dict[str, str]:
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if not self.api_key:
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return {"error": "Missing API Key"}
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prompt = f"""
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You are analyzing a user's Spotify listening data. Below is a JSON summary of metrics I've computed. Your job is to:
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1. Write a narrative "Vibe Check" (2-3 paragraphs) describing their overall listening personality this period.
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2. Identify 3-5 notable patterns or anomalies.
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3. Provide a "Musical Persona" label (e.g., "Late-Night Binge Listener", "Genre Chameleon", "Album Purist").
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4. Write a brief, playful "roast" (1-2 sentences) based on the data.
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Guidelines:
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- Do NOT recalculate any numbers.
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- Use specific metrics to support observations (e.g., "Your whiplash score of 18.3 BPM suggests...").
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- Keep tone conversational but insightful.
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- Avoid mental health claims; stick to behavioral descriptors.
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- Highlight both positive patterns and quirks.
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Data:
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{json.dumps(stats_json, indent=2)}
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Output Format (return valid JSON):
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{{
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"vibe_check": "...",
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"patterns": ["...", "..."],
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"persona": "...",
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"roast": "..."
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}}
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"""
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try:
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# Handle full model path if passed or default short name
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# The library often accepts 'gemini-2.5-flash' but list_models returns 'models/gemini-2.5-flash'
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model_id = self.model_name
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if not model_id.startswith("models/") and "/" not in model_id:
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# Try simple name, if it fails user might need to pass 'models/...'
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pass
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model = genai.GenerativeModel(model_id)
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response = model.generate_content(prompt)
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# Clean up response to ensure valid JSON (sometimes LLMs add markdown blocks)
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text = response.text.strip()
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if text.startswith("```json"):
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text = text.replace("```json", "").replace("```", "")
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elif text.startswith("```"):
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text = text.replace("```", "")
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return json.loads(text)
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except Exception as e:
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return {"error": str(e), "raw_response": response.text if 'response' in locals() else "No response"}
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