Remove remaining binary file for Hugging Face

src/news_analysis.py

@@ -1,3 +1,171 @@
+# import os
+# import csv
+# from datetime import datetime
+# from langchain_openai import ChatOpenAI
+# from langchain_core.prompts import PromptTemplate
+# from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate
+# import requests
+# from dotenv import load_dotenv
+# from fin_interpreter import analyze_article
+# from tavily import TavilyClient
+
+# # === Load environment or passed keys ===
+# load_dotenv()
+# OPENAI_KEY = os.environ.get("OPENAI_API_KEY") or os.getenv("OPENAI_KEY")
+# TAVILY_KEY = os.environ.get("TAVILY_API_KEY") or os.getenv("TAVILY_KEY")
+
+# # === Initialize Tavily Client ===
+# tavily_client = TavilyClient(api_key=TAVILY_KEY)
+
+# # === Get OpenAI client when needed ===
+# def get_llm():
+#     openai_key = os.environ.get("OPENAI_API_KEY")
+#     if not openai_key:
+#         raise ValueError("OPENAI_API_KEY not found.")
+#     return ChatOpenAI(model_name="gpt-4.1", openai_api_key=openai_key)
+
+# # === Related Terms ===
+# def get_related_terms(topic):
+#     llm = get_llm()
+#     prompt = f"What are 5 closely related financial or industry terms to '{topic}'?"
+#     response = llm.invoke(prompt)
+#     return response.content.split(",")
+
+# def tavily_search(query, days, max_results=10):
+#     api_key = os.getenv("TAVILY_KEY")
+#     url = "https://api.tavily.com/search"
+#     headers = {"Authorization": f"Bearer {api_key}"}
+#     payload = {
+#         "query": query,
+#         "search_depth": "advanced",
+#         "topic": "news",
+#         "days": int(days),
+#         "max_results": max_results,
+#         "include_answer": False,
+#         "include_raw_content": False
+#     }
+#     response = requests.post(url, json=payload, headers=headers)
+#     return response.json()
+
+# # === Smart News Search ===
+# def fetch_deep_news(topic, days):
+#     all_results = []
+#     seen_urls = set()
+
+#     base_queries = [
+#         topic,
+#         f"{topic} AND startup",
+#         f"{topic} AND acquisition OR merger OR funding",
+#         f"{topic} AND CEO OR executive OR leadership",
+#         f"{topic} AND venture capital OR Series A OR Series B",
+#         f"{topic} AND government grant OR approval OR contract",
+#         f"{topic} AND underrated OR small-cap OR micro-cap"
+#     ]
+
+#     investor_queries = [
+#         f"{topic} AND BlackRock OR Vanguard OR SoftBank",
+#         f"{topic} AND Elon Musk OR Sam Altman OR Peter Thiel",
+#         f"{topic} AND Berkshire Hathaway OR Warren Buffett",
+#         f"{topic} AND institutional investor OR hedge fund",
+#     ]
+
+#     related_terms = get_related_terms(topic)
+#     synonym_queries = [f"{term} AND {kw}" for term in related_terms for kw in ["startup", "funding", "merger", "acquisition"]]
+
+#     all_queries = base_queries + investor_queries + synonym_queries
+
+#     for query in all_queries:
+#         try:
+#             print(f"🔍 Tavily query: {query}")
+#             response = requests.post(
+#                 url="https://api.tavily.com/search",
+#                 headers={
+#                     "Authorization": f"Bearer {TAVILY_KEY}",
+#                     "Content-Type": "application/json"
+#                 },
+#                 json={
+#                     "query": query,
+#                     "search_depth": "advanced",
+#                     "topic": "news",
+#                     "days": int(days),
+#                     "max_results": 10,
+#                     "include_answer": False,
+#                     "include_raw_content": False
+#                 }
+#             )
+
+#             if response.status_code != 200:
+#                 print(f"⚠️ Tavily API error: {response.status_code} - {response.text}")
+#                 continue
+
+#             for item in response.json().get("results", []):
+#                 url = item.get("url")
+#                 content = item.get("content", "") or item.get("summary", "") or item.get("title", "")
+#                 if url and url not in seen_urls and len(content) > 150:
+#                     all_results.append({
+#                         "title": item.get("title"),
+#                         "url": url,
+#                         "content": content
+#                     })
+#                     seen_urls.add(url)
+
+#         except Exception as e:
+#             print(f"⚠️ Tavily request failed for query '{query}': {e}")
+
+#     print(f"📰 Total articles collected: {len(all_results)}")
+#     return all_results
+
+# # === Generate Markdown Report ===
+# def generate_value_investor_report(topic, news_results, max_articles=20, max_chars_per_article=400):
+#     news_results = news_results[:max_articles]
+
+#     for item in news_results:
+#         result = analyze_article(item["content"])
+#         item["fin_sentiment"] = result.get("sentiment", "neutral")
+#         item["fin_confidence"] = result.get("confidence", 0.0)
+#         item["investment_decision"] = result.get("investment_decision", "Watch")
+
+#     article_summary = "".join(
+#         f"- **{item['title']}**: {item['content'][:max_chars_per_article]}... "
+#         f"(Sentiment: {item['fin_sentiment'].title()}, Confidence: {item['fin_confidence']:.2f}, "
+#         f"Decision: {item['investment_decision']}) [link]({item['url']})\n"
+#         for item in news_results
+#     )
+
+#     prompt = PromptTemplate.from_template("""
+# You're a highly focused value investor. Analyze this week's news on "{Topic}".
+
+# Your goal is to uncover:
+# - Meaningful events (e.g., CEO joining a startup, insider buys, big-name partnerships)
+# - Startups or small caps that may signal undervalued opportunity
+# - Connections to key individuals or institutions (e.g., Elon Musk investing, Sam Altman joining)
+# - Companies with strong fundamentals: low P/E, low P/B, high ROE, recent IPOs, moats, or high free cash flow
+
+# ### News
+# {ArticleSummaries}
+
+# Write a markdown memo with:
+# 1. **Key Value Signals**
+# 2. **Stocks or Startups to Watch**
+# 3. **What Smart Money Might Be Acting On**
+# 4. **References**
+# 5. **Investment Hypothesis**
+
+# Include context and macroeconomic/regulatory angles. Add an intro on sentiment and market trends for the week.
+# """)
+
+#     chat_prompt = ChatPromptTemplate.from_messages([
+#         SystemMessagePromptTemplate(prompt=prompt)
+#     ])
+#     prompt_value = chat_prompt.format_prompt(
+#         Topic=topic,
+#         ArticleSummaries=article_summary
+#     ).to_messages()
+
+#     llm = get_llm()
+#     result = llm.invoke(prompt_value)
+#     return result.content
+
 import os
 import csv
 from datetime import datetime
@@ -7,15 +175,11 @@ from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate
 import requests
 from dotenv import load_dotenv
 from fin_interpreter import analyze_article
-from tavily import TavilyClient
 
-# === Load environment or passed keys ===
+# === Load environment ===
 load_dotenv()
 OPENAI_KEY = os.environ.get("OPENAI_API_KEY") or os.getenv("OPENAI_KEY")
-TAVILY_KEY = os.environ.get("TAVILY_API_KEY") or os.getenv("TAVILY_KEY")
-
-# === Initialize Tavily Client ===
-tavily_client = TavilyClient(api_key=TAVILY_KEY)
+TAVILY_KEY = None  # Will be accessed dynamically at runtime
 
 # === Get OpenAI client when needed ===
 def get_llm():
@@ -31,8 +195,9 @@ def get_related_terms(topic):
     response = llm.invoke(prompt)
     return response.content.split(",")
 
+# === Tavily Search ===
 def tavily_search(query, days, max_results=10):
-    api_key = os.getenv("TAVILY_KEY")
+    api_key = os.environ.get("TAVILY_API_KEY") or TAVILY_KEY
     url = "https://api.tavily.com/search"
     headers = {"Authorization": f"Bearer {api_key}"}
     payload = {
@@ -77,28 +242,13 @@ def fetch_deep_news(topic, days):
     for query in all_queries:
         try:
             print(f"🔍 Tavily query: {query}")
-            response = requests.post(
-                url="https://api.tavily.com/search",
-                headers={
-                    "Authorization": f"Bearer {TAVILY_KEY}",
-                    "Content-Type": "application/json"
-                },
-                json={
-                    "query": query,
-                    "search_depth": "advanced",
-                    "topic": "news",
-                    "days": int(days),
-                    "max_results": 10,
-                    "include_answer": False,
-                    "include_raw_content": False
-                }
-            )
-
-            if response.status_code != 200:
-                print(f"⚠️ Tavily API error: {response.status_code} - {response.text}")
+            response = tavily_search(query, days)
+
+            if not isinstance(response, dict) or "results" not in response:
+                print(f"⚠️ Tavily API response issue: {response}")
                 continue
 
-            for item in response.json().get("results", []):
+            for item in response.get("results", []):
                 url = item.get("url")
                 content = item.get("content", "") or item.get("summary", "") or item.get("title", "")
                 if url and url not in seen_urls and len(content) > 150: