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"""
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.
- Synthesis of multiple news items into a daily briefing.
"""
import os
import logging
import httpx
import json
from typing import Optional, TypedDict, List, Union
# Configure logging
logger = logging.getLogger(__name__)
# --- Type Definitions for Structured Data ---
class AnalysisResult(TypedDict):
sentiment: str
sentiment_score: float
reason: str
entities: List[str]
topic: str
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."""
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_analysis_prompt(self, text: str) -> dict:
"""Creates the structured JSON prompt for analyzing a single piece of text."""
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_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()
full_response = response.json()
json_text = full_response["candidates"][0]["content"]["parts"][0]["text"]
analysis: AnalysisResult = json.loads(json_text)
analysis["error"] = None
return analysis
except Exception as 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 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." |