Create gemini_analyzer.py

app/gemini_analyzer.py

@@ -0,0 +1,90 @@
+"""
+A sophisticated analyzer using the Google Gemini Pro API.
+
+This module provides structured analysis of financial text, including:
+- Nuanced sentiment with reasoning.
+- Key entity extraction (e.g., cryptocurrencies).
+- Topic classification.
+- Potential market impact assessment.
+"""
+import os
+import logging
+import httpx
+from typing import Optional, TypedDict, List
+
+# Configure logging
+logger = logging.getLogger(__name__)
+
+# --- Pydantic-like models for structured output ---
+class AnalysisResult(TypedDict):
+    sentiment: str
+    sentiment_score: float
+    reason: str
+    entities: List[str]
+    topic: str
+    impact: str
+    summary: str
+    error: Optional[str]
+
+class GeminiAnalyzer:
+    """Manages interaction with the Google Gemini API for deep text analysis."""
+
+    API_URL = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro-latest:generateContent"
+
+    def __init__(self, client: httpx.AsyncClient, api_key: Optional[str] = None):
+        self.client = client
+        self.api_key = api_key or os.getenv("GEMINI_API_KEY")
+        if not self.api_key:
+            raise ValueError("GEMINI_API_KEY is not set. Please add it as a repository secret.")
+        self.params = {"key": self.api_key}
+        self.headers = {"Content-Type": "application/json"}
+
+    def _build_prompt(self, text: str) -> dict:
+        """Creates the structured JSON prompt for the Gemini API."""
+        # This is where the magic happens. We're "prompt engineering" Gemini.
+        return {
+            "contents": [{
+                "parts": [{
+                    "text": f"""
+                    Analyze the following financial text from the cryptocurrency world.
+                    Provide your analysis as a single, minified JSON object with NO markdown formatting.
+
+                    The JSON object must have these exact keys: "sentiment", "sentiment_score", "reason", "entities", "topic", "impact", "summary".
+
+                    - "sentiment": MUST be one of "POSITIVE", "NEGATIVE", or "NEUTRAL".
+                    - "sentiment_score": A float between -1.0 (very negative) and 1.0 (very positive).
+                    - "reason": A brief, one-sentence explanation for the sentiment score.
+                    - "entities": A JSON array of strings listing the primary cryptocurrencies or tokens mentioned (e.g., ["Bitcoin", "ETH"]).
+                    - "topic": MUST be one of "Regulation", "Partnership", "Technical Update", "Market Hype", "Security", or "General News".
+                    - "impact": Assess the potential short-term market impact. MUST be one of "LOW", "MEDIUM", or "HIGH".
+                    - "summary": A concise, one-sentence summary of the provided text.
+
+                    Text to analyze: "{text}"
+                    """
+                }]
+            }]
+        }
+
+    async def analyze_text(self, text: str) -> AnalysisResult:
+        """Sends text to Gemini and returns a structured analysis."""
+        prompt = self._build_prompt(text)
+        try:
+            response = await self.client.post(self.API_URL, headers=self.headers, params=self.params, json=prompt, timeout=60.0)
+            response.raise_for_status()
+
+            # Extract the JSON content from the response
+            full_response = response.json()
+            json_text = full_response["candidates"][0]["content"]["parts"][0]["text"]
+            
+            # The output is a JSON string, so we parse it.
+            analysis = json.loads(json_text)
+            analysis["error"] = None
+            return analysis
+
+        except Exception as e:
+            logger.error(f"❌ Gemini API Error: {e}")
+            return {
+                "sentiment": "ERROR", "sentiment_score": 0, "reason": str(e),
+                "entities": [], "topic": "Unknown", "impact": "Unknown",
+                "summary": "Failed to analyze text.", "error": str(e)
+            }