Update app/gemini_analyzer.py

app/gemini_analyzer.py

@@ -6,22 +6,18 @@ This module provides structured analysis of financial text, including:
 - Key entity extraction (e.g., cryptocurrencies).
 - Topic classification.
 - Potential market impact assessment.
+- Synthesis of multiple news items into a daily briefing.
 """
 import os
 import logging
 import httpx
-# ====================================================================
-#                       FIX APPLIED HERE
-# ====================================================================
-# Import the missing 'json' module
 import json
-# ====================================================================
-from typing import Optional, TypedDict, List
+from typing import Optional, TypedDict, List, Union
 
 # Configure logging
 logger = logging.getLogger(__name__)
 
-# --- Pydantic-like models for structured output ---
+# --- Type Definitions for Structured Data ---
 class AnalysisResult(TypedDict):
     sentiment: str
     sentiment_score: float
@@ -31,6 +27,7 @@ class AnalysisResult(TypedDict):
     impact: str
     summary: str
     error: Optional[str]
+    url: Optional[str] # To store the article URL
 
 class GeminiAnalyzer:
     """Manages interaction with the Google Gemini API for deep text analysis."""
@@ -45,8 +42,8 @@ class GeminiAnalyzer:
         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."""
+    def _build_analysis_prompt(self, text: str) -> dict:
+        """Creates the structured JSON prompt for analyzing a single piece of text."""
         return {
             "contents": [{
                 "parts": [{
@@ -72,24 +69,64 @@ class GeminiAnalyzer:
 
     async def analyze_text(self, text: str) -> AnalysisResult:
         """Sends text to Gemini and returns a structured analysis."""
-        prompt = self._build_prompt(text)
+        prompt = self._build_analysis_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: AnalysisResult = json.loads(json_text)
             analysis["error"] = None
             return analysis
 
         except Exception as e:
-            logger.error(f"❌ Gemini API Error: {e}")
+            logger.error(f"❌ Gemini Analysis Error: {e}")
             return {
                 "sentiment": "ERROR", "sentiment_score": 0, "reason": str(e),
                 "entities": [], "topic": "Unknown", "impact": "Unknown",
-                "summary": "Failed to analyze text.", "error": str(e)
-            }
+                "summary": "Failed to analyze text due to an API or parsing error.", "error": str(e)
+            }
+
+    async def generate_daily_briefing(self, analysis_items: List[dict]) -> str:
+        """Generates a high-level market briefing from a list of analyzed news items."""
+        if not analysis_items:
+            return "### Briefing Unavailable\nNo news items were analyzed in the last period."
+
+        context = "\n".join([f"- {item.get('summary')} (Impact: {item.get('impact')}, Topic: {item.get('topic')})" for item in analysis_items])
+
+        briefing_prompt = {
+            "contents": [{
+                "parts": [{
+                    "text": f"""
+                    You are a senior crypto market analyst named 'Sentinel'. Your tone is professional, concise, and insightful.
+                    Based on the following list of analyzed news items from the last 24 hours, write a "Daily Market Briefing".
+
+                    The briefing must have three sections using markdown:
+                    1. "### Executive Summary": A single, impactful paragraph summarizing the overall market mood and key events.
+                    2. "### Top Bullish Signals": 2-3 bullet points on the most positive developments.
+                    3. "### Top Bearish Signals": 2-3 bullet points on the most significant risks or negative news.
+
+                    Here is the data to analyze:
+                    {context}
+                    """
+                }]
+            }],
+            "safetySettings": [
+                {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
+                {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
+                {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
+                {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"},
+            ]
+        }
+        
+        try:
+            response = await self.client.post(self.API_URL, headers=self.headers, params=self.params, json=briefing_prompt, timeout=120.0)
+            response.raise_for_status()
+            full_response = response.json()
+            briefing_text = full_response["candidates"][0]["content"]["parts"][0]["text"]
+            return briefing_text
+        except Exception as e:
+            logger.error(f"❌ Gemini Briefing Error: {e}")
+            return "### Briefing Unavailable\nCould not generate the daily market briefing due to a Gemini API error."