Update app.py

app.py

@@ -452,104 +452,108 @@ if activities == "Symbol Analysis":
             st.write("Raw text was:")
             st.text(res["output_text"])
 elif activities=="News Sentiment":
-    url1 = f"https://economictimes.indiatimes.com/markets/stocks/news"
-    url2 = f"https://www.livemint.com/market/stock-market-news/"
-    url3 = f"https://in.tradingview.com/ideas/editors-picks/?type=trade"
-    url4 = f"https://pulse.zerodha.com/"
-    url5 = "https://upstox.com/news/market-news/stocks/"
-    # url6 = "https://trendlyne.com/market-insights/"
 
-    loader = WebBaseLoader([url1,
-                            url2,
-                            url3,
-                            url4,
-                            url5,
-                            # url6
-                           ])
-    docs = loader.load()
-    # st.write(docs)
-    st.divider()
-
-    llm_prompt_template = """You are an expert Stock Market Trader specializing in stock market insights derived from fundamental analysis, analytical trends, profit-based evaluations, news indicators from different sites and detailed company financials. 
-    You will receive stock market news articles or stocks in news from various news websites which have India stock news feed. For the below context/input_documents, perform the following tasks:
-
-    Context:
-    {input_documents}
 
-    1. **Top picks**: After analyzing all provided data, rank the top 5-10 stocks to look at this week, including tickers, current sentiment, and why they made the list.
-    2. **Identify the stock(s)** mentioned (by ticker and company name).
-    3. **Sentiment analysis**: classify as Bullish, Bearish, or Neutral.
-    4. **Extract critical news**: What is the main event or update? (e.g., earnings beat, regulatory approval, management change, major contract or macro impact).
-    5. **Summarize impact**: Briefly explain how this news might affect stock price and investor behavior (e.g., “could boost investor confidence”, “sign indicates profit pressure”, etc.).
-    6. **Actionable signal**: Based on the sentiment and news, suggest whether this is a “Buy”, “Sell”, “Hold”, or “Watch” recommendation, and the rationale.
-
-    PROVIDE THE DETAILS based on just the FACTS present in the document. Do NOT DUPLICATE the Output & hallucinate.
-    ***Format your output as JSON*** with the following structure:
+    if st.button("Get Live Updates..."):
+        url1 = f"https://economictimes.indiatimes.com/markets/stocks/news"
+        url2 = f"https://www.livemint.com/market/stock-market-news/"
+        url3 = f"https://in.tradingview.com/ideas/editors-picks/?type=trade"
+        url4 = f"https://pulse.zerodha.com/"
+        url5 = "https://upstox.com/news/market-news/stocks/"
+        # url6 = "https://trendlyne.com/market-insights/"
     
-    ```json
-    {{
-      "top_picks": [
-        {{
-          "ticker": "TICKER",
-          "company": "Company Name",
-          "sentiment": "Bullish|Bearish|Neutral",
-          "critical_news": "Brief summary of the key event",
-          "impact_summary": "How this may affect the stock",
-          "action": "Buy|Sell|Hold|Watch",
-          "reason": "Why this stock ranks among top picks"
-        }},
-        ...
-      ]
-    }}
-
-    """
-
-
-    google_docs = get_google_news_documents("Indian Stock market news NSE, Stocks in Action, Stocks in News, Stocks to Buy in next few weeks", max_articles=10)
-    docs.extend(google_docs)
-    # st.write(docs)
-    llm_prompt = PromptTemplate.from_template(llm_prompt_template)
-
-    llm_chain = LLMChain(llm=llm,prompt=llm_prompt)
-    stuff_chain = StuffDocumentsChain(llm_chain=llm_chain,document_variable_name="input_documents")
+        loader = WebBaseLoader([url1,
+                                url2,
+                                url3,
+                                url4,
+                                url5,
+                                # url6
+                               ])
+        docs = loader.load()
+        # st.write(docs)
+        st.divider()
     
-    # res = stuff_chain.invoke(docs)
-    res = stuff_chain.invoke({"input_documents": docs})
-    raw_text = res["output_text"]
-    # Remove markdown code block delimiters if present
-    if raw_text.startswith("```json"):
-        raw_text = raw_text[len("```json"):]
+        llm_prompt_template = """You are an expert Stock Market Trader specializing in stock market insights derived from fundamental analysis, analytical trends, profit-based evaluations, news indicators from different sites and detailed company financials. 
+        You will receive stock market news articles or stocks in news from various news websites which have India stock news feed. For the below context/input_documents, perform the following tasks:
     
-    if raw_text.endswith("```"):
-        raw_text = raw_text[:-3]
+        Context:
+        {input_documents}
     
-    # Also strip leading/trailing whitespace/newlines
-    raw_text = raw_text.strip()
-
-    # Parse JSON
-    parsed_data = json.loads(raw_text)
-    top_picks = parsed_data.get("top_picks", [])
+        1. **Top picks**: After analyzing all provided data, rank the top 5-10 stocks to look at this week, including tickers, current sentiment, and why they made the list.
+        2. **Identify the stock(s)** mentioned (by ticker and company name).
+        3. **Sentiment analysis**: classify as Bullish, Bearish, or Neutral.
+        4. **Extract critical news**: What is the main event or update? (e.g., earnings beat, regulatory approval, management change, major contract or macro impact).
+        5. **Summarize impact**: Briefly explain how this news might affect stock price and investor behavior (e.g., “could boost investor confidence”, “sign indicates profit pressure”, etc.).
+        6. **Actionable signal**: Based on the sentiment and news, suggest whether this is a “Buy”, “Sell”, “Hold”, or “Watch” recommendation, and the rationale.
     
+        PROVIDE THE DETAILS based on just the FACTS present in the document. Do NOT DUPLICATE the Output & hallucinate.
+        ***Format your output as JSON*** with the following structure:
+        
+        ```json
+        {{
+          "top_picks": [
+            {{
+              "ticker": "TICKER",
+              "company": "Company Name",
+              "sentiment": "Bullish|Bearish|Neutral",
+              "critical_news": "Brief summary of the key event",
+              "impact_summary": "How this may affect the stock",
+              "action": "Buy|Sell|Hold|Watch",
+              "reason": "Why this stock ranks among top picks"
+            }},
+            ...
+          ]
+        }}
     
-    # Layout
-    for stock in top_picks:
-        st.subheader(f"{stock['company']} ({stock['ticker']})")
-        col1,col2,col3, col4 = st.columns([1,1,1, 1])
-        with col1:
-            st.markdown(f"**📰 Critical News:** {stock['critical_news']}")
-        with col2:
-            st.markdown(f"**📈 Impact Summary:** {stock['impact_summary']}")
-        with col3:
-            st.markdown(f"**💡 Reason for Top Pick:** {stock['reason']}")
-        with col4:
-            sentiment_color = {
-                "Bullish": "🟢 Bullish",
-                "Bearish": "🔴 Bearish",
-                "Neutral": "🟡 Neutral"
-            }.get(stock["sentiment"], stock["sentiment"])
-            st.metric(label="Sentiment", value=sentiment_color)
-            st.markdown(f"**🚦 Action:** :red[{stock['action']}]")
+        """
     
+    
+        google_docs = get_google_news_documents("Indian Stock market news NSE, Stocks in Action, Stocks in News, Stocks to Buy in next few weeks", max_articles=10)
+        docs.extend(google_docs)
+        # st.write(docs)
+        llm_prompt = PromptTemplate.from_template(llm_prompt_template)
+    
+        llm_chain = LLMChain(llm=llm,prompt=llm_prompt)
+        stuff_chain = StuffDocumentsChain(llm_chain=llm_chain,document_variable_name="input_documents")
+        
+        # res = stuff_chain.invoke(docs)
+        res = stuff_chain.invoke({"input_documents": docs})
+        raw_text = res["output_text"]
+        # Remove markdown code block delimiters if present
+        if raw_text.startswith("```json"):
+            raw_text = raw_text[len("```json"):]
+        
+        if raw_text.endswith("```"):
+            raw_text = raw_text[:-3]
+        
+        # Also strip leading/trailing whitespace/newlines
+        raw_text = raw_text.strip()
+    
+        # Parse JSON
+        parsed_data = json.loads(raw_text)
+        top_picks = parsed_data.get("top_picks", [])
+        
+        
+        # Layout
+        for stock in top_picks:
+            st.subheader(f"{stock['company']} ({stock['ticker']})")
+            col1,col2,col3, col4 = st.columns([1,1,1, 1])
+            with col1:
+                st.markdown(f"**📰 Critical News:** {stock['critical_news']}")
+            with col2:
+                st.markdown(f"**📈 Impact Summary:** {stock['impact_summary']}")
+            with col3:
+                st.markdown(f"**💡 Reason for Top Pick:** {stock['reason']}")
+            with col4:
+                sentiment_color = {
+                    "Bullish": "🟢 Bullish",
+                    "Bearish": "🔴 Bearish",
+                    "Neutral": "🟡 Neutral"
+                }.get(stock["sentiment"], stock["sentiment"])
+                st.metric(label="Sentiment", value=sentiment_color)
+                st.markdown(f"**🚦 Action:** :red[{stock['action']}]")
+    else:
+        pass
     st.divider()