Update app.py

app.py

@@ -66,13 +66,14 @@ if ticker_user!="":
     # Based on the context below
     # {context}, Summarize the stock based on Historical data based on fundamental, price, news, sentiment , any red flags and suggest rating of the Stock in a 1 to 10 Scale"""
     
-    llm_prompt_template = """You are an expert Stock Market Trader specializing in stock market insights derived from fundamental analysis, analytical trends, profit-based evaluations, and detailed company financials. Using your expertise, please analyze the stock based on the provided context below.
+    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. 
+    Using your expertise, please analyze the stock based on the provided context below.
     
     Context:
     {input_documents}
     
     Task:
-    Summarize the stock based on its historical and current data.
+    Summarize the stock based on its historical and current data. Keep it CONCISE & BRIEF.
     Evaluate the stock on the following parameters:
     1. Company Fundamentals: Assess the stock's intrinsic value, growth potential, and financial health.
     2. Current & Future Price Trends: Analyze historical price movements and current price trends.
@@ -237,148 +238,133 @@ if ticker_user!="":
     # st.subheader("🔍 Indicators")
     # st.dataframe(analysis_summary.indicators, use_container_width=True)
 
-    url = "https://api.chart-img.com/v2/tradingview/advanced-chart"
-    api_key = "l0iUFRSeqC9z7nDPTd1hnafPh2RrdcEy6rl6tNqV"
-    headers = {
-        "x-api-key": api_key,
-        "content-type": "application/json"
-    }
-    data = {
-        "height": 400,
-        "theme": "light",
-        "interval": "1D",
-        "session": "extended",
-        "symbol": f"NSE:{ticker_user}"
-    }
+    # url = "https://api.chart-img.com/v2/tradingview/advanced-chart"
+    # api_key = "l0iUFRSeqC9z7nDPTd1hnafPh2RrdcEy6rl6tNqV"
+    # headers = {
+    #     "x-api-key": api_key,
+    #     "content-type": "application/json"
+    # }
+    # data = {
+    #     "height": 400,
+    #     "theme": "light",
+    #     "interval": "1D",
+    #     "session": "extended",
+    #     "symbol": f"NSE:{ticker_user}"
+    # }
+    
+    # response = requests.post(url, headers=headers, json=data)
     
-    response = requests.post(url, headers=headers, json=data)
+    # if response.status_code == 200:
+    #     with open("chart_t1.jpg", "wb") as f:
+    #         f.write(response.content)
+    #     with col2:
+    #         st.image("chart_t1.jpg", caption='')
+    # else:
+    #     st.warning(f"Failed to retrieve image. Status code: {response.status_code}")
+    #     st.warning("Response:", response.text)
     
-    if response.status_code == 200:
-        with open("chart_t1.jpg", "wb") as f:
-            f.write(response.content)
-        with col2:
-            st.image("chart_t1.jpg", caption='')
+    llm_prompt = PromptTemplate.from_template(llm_prompt_template)
 
-        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")
     
-        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})
-        #create the humanmassage propmt templete with the image file 
-        # hmessage = HumanMessage(
-        #     content=[
-        #         {
-        #             "type": "text",
-        #             "text": "Based on the Image, suggest a BUY and SELL Strategy along with Risk based approach using Stop loss/Target price. PROVIDE THE DETAILS based on just the FACTS present and PROVIDE THE DETAILS IN an JSON Object",
-        #         },
-        #         {"type": "image_url", "image_url": "chart_t1.jpg"},
-        #     ]
-        # )
-        # message = llm_vis.invoke([hmessage])
-        
-        # st.write(message.content)
+    # res = stuff_chain.invoke(docs)
+    res = stuff_chain.invoke({"input_documents": docs})
+    try:
+        raw_text = res["output_text"]
 
+        # Remove markdown code block delimiters if present
+        if raw_text.startswith("```json"):
+            raw_text = raw_text[len("```json"):]
         
-        try:
-            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()
+        if raw_text.endswith("```"):
+            raw_text = raw_text[:-3]
+        
+        # Also strip leading/trailing whitespace/newlines
+        raw_text = raw_text.strip()
 
 
-            data = json.loads(raw_text)
-            # data = res["output_text"]
-            # Header Info
-            st.markdown(f"### {data['stock_summary']['company_name']} ({data['stock_summary']['ticker']}) | {data['stock_summary']['exchange']}")
-            st.markdown(f"**Description**: {data['stock_summary']['description']}")
-            
-            # === Row 1: Price and Market Cap ===
-            row1 = st.columns(3)
-            row1[0].metric("💰 Current Price", data["stock_summary"]["current_price"])
-            row1[1].metric("🏢 Market Cap", data["stock_summary"]["market_cap"])
-            row1[2].metric("⭐ Rating", data["overall_rating"]["rating"])
-            
-            # === Row 2: Historical Performance ===
-            st.subheader("📊 Historical Performance")
-            perf_cols = st.columns(len(data["stock_summary"]["historical_performance"]))
-            for i, (k, v) in enumerate(data["stock_summary"]["historical_performance"].items()):
-                perf_cols[i].metric(k.replace("_", " ").title(), v)
-            
-            # === Row 3: Fundamentals ===
-            st.subheader("📘 Company Fundamentals")
-            row3 = st.columns(4)
-            metrics = data["evaluation_parameters"]["company_fundamentals"]["key_metrics"]
-            row3[0].metric("P/E Ratio", metrics["pe_ratio"])
-            row3[1].metric("EPS YoY", metrics["eps_growth_yoy"])
-            row3[2].metric("Revenue YoY", metrics["revenue_growth_yoy"])
-            row3[3].metric("Dividend Yield", metrics["dividend_yield"])
-            
-            row3b = st.columns(4)
-            row3b[0].metric("Net Income YoY", metrics["net_income_growth_yoy"])
-            row3b[1].metric("Volume", metrics["volume"])
-            row3b[2].metric("Return on Capital", metrics["return_on_capital"])
-            row3b[3].metric("Balance Sheet", metrics["balance_sheet"])
-            
-            st.info(data["evaluation_parameters"]["company_fundamentals"]["assessment"])
-            
-            # === Row 4: Trends and Technicals ===
-            st.subheader("📈 Trends & Technical Analysis")
-            row4 = st.columns(3)
-            row4[0].markdown(f"**Historical Trends:** {data['evaluation_parameters']['current_and_future_price_trends']['historical_trends']}")
-            row4[1].markdown(f"**Current Trends:** {data['evaluation_parameters']['current_and_future_price_trends']['current_trends']}")
-            row4[2].markdown(f"**Technical Indicators:** {data['evaluation_parameters']['current_and_future_price_trends']['technical_indicators']}")
-            
-            st.success(data["evaluation_parameters"]["current_and_future_price_trends"]["assessment"])
-            st.caption(f"📝 Notes: {data['evaluation_parameters']['current_and_future_price_trends']['technical_analysis_notes']}")
-            
-            # === Row 5: Sentiment ===
-            st.subheader("📰 News & Sentiment")
-            sentiment_cols = st.columns(2)
-            with sentiment_cols[0]:
-                st.success("👍 Positive Sentiment")
-                for s in data["evaluation_parameters"]["news_and_sentiment"]["positive_sentiment"]:
-                    st.write(f"✅ {s}")
-            with sentiment_cols[1]:
-                st.error("👎 Negative Sentiment")
-                for s in data["evaluation_parameters"]["news_and_sentiment"]["negative_sentiment"]:
-                    st.write(f"❌ {s}")
-            st.info(data["evaluation_parameters"]["news_and_sentiment"]["assessment"])
-            
-            # === Row 6: Red Flags ===
-            st.subheader("🚩 Red Flags")
-            red_flag_cols = st.columns(3)
-            for i, flag in enumerate(data["evaluation_parameters"]["red_flags"]):
-                red_flag_cols[i].warning(f"**{flag['flag']}**\n{flag['details']}")
-            
-            # === Row 7: Investment Advice ===
-            st.subheader("💡 Investment Advice")
-            advice_cols = st.columns(3)
-            advice = data["investment_advice"]
-            advice_cols[0].markdown(f"**Next 1 Week**\n{advice['next_1_weeks_outlook']}")
-            advice_cols[1].markdown(f"**Next 5 Weeks**\n{advice['next_5_weeks_outlook']}")
-            advice_cols[2].markdown(f"**Next 10 Weeks**\n{advice['next_10_weeks_outlook']}")
-            
-            action_cols = st.columns(3)
-            action_cols[0].success(f"**Buy:** {advice['price_action_suggestions']['buy']}")
-            action_cols[1].info(f"**Hold:** {advice['price_action_suggestions']['hold']}")
-            action_cols[2].error(f"**Sell:** {advice['price_action_suggestions']['sell']}")
-            
-            # === Footer ===
-            st.markdown("---")
-            st.caption("Generated by AI-powered financial analysis dashboard.")
-        except json.JSONDecodeError as e:
-            st.error(f"JSON decode error: {e}")
-            st.write("Raw text was:")
-            st.text(res["output_text"])
-    else:
-        st.warning(f"Failed to retrieve image. Status code: {response.status_code}")
-        st.warning("Response:", response.text)
+        data = json.loads(raw_text)
+        # data = res["output_text"]
+        # Header Info
+        st.markdown(f"### {data['stock_summary']['company_name']} ({data['stock_summary']['ticker']}) | {data['stock_summary']['exchange']}")
+        st.markdown(f"**Description**: {data['stock_summary']['description']}")
+        
+        # === Row 1: Price and Market Cap ===
+        row1 = st.columns(3)
+        row1[0].metric("💰 Current Price", data["stock_summary"]["current_price"])
+        row1[1].metric("🏢 Market Cap", data["stock_summary"]["market_cap"])
+        row1[2].metric("⭐ Rating", data["overall_rating"]["rating"])
+        
+        # === Row 2: Historical Performance ===
+        st.subheader("📊 Historical Performance")
+        perf_cols = st.columns(len(data["stock_summary"]["historical_performance"]))
+        for i, (k, v) in enumerate(data["stock_summary"]["historical_performance"].items()):
+            perf_cols[i].metric(k.replace("_", " ").title(), v)
+        
+        # === Row 3: Fundamentals ===
+        st.subheader("📘 Company Fundamentals")
+        row3 = st.columns(4)
+        metrics = data["evaluation_parameters"]["company_fundamentals"]["key_metrics"]
+        row3[0].metric("P/E Ratio", metrics["pe_ratio"])
+        row3[1].metric("EPS YoY", metrics["eps_growth_yoy"])
+        row3[2].metric("Revenue YoY", metrics["revenue_growth_yoy"])
+        row3[3].metric("Dividend Yield", metrics["dividend_yield"])
+        
+        row3b = st.columns(4)
+        row3b[0].metric("Net Income YoY", metrics["net_income_growth_yoy"])
+        row3b[1].metric("Volume", metrics["volume"])
+        row3b[2].metric("Return on Capital", metrics["return_on_capital"])
+        row3b[3].metric("Balance Sheet", metrics["balance_sheet"])
+        
+        st.info(data["evaluation_parameters"]["company_fundamentals"]["assessment"])
+        
+        # === Row 4: Trends and Technicals ===
+        st.subheader("📈 Trends & Technical Analysis")
+        row4 = st.columns(3)
+        row4[0].markdown(f"**Historical Trends:** {data['evaluation_parameters']['current_and_future_price_trends']['historical_trends']}")
+        row4[1].markdown(f"**Current Trends:** {data['evaluation_parameters']['current_and_future_price_trends']['current_trends']}")
+        row4[2].markdown(f"**Technical Indicators:** {data['evaluation_parameters']['current_and_future_price_trends']['technical_indicators']}")
+        
+        st.success(data["evaluation_parameters"]["current_and_future_price_trends"]["assessment"])
+        st.caption(f"📝 Notes: {data['evaluation_parameters']['current_and_future_price_trends']['technical_analysis_notes']}")
+        
+        # === Row 5: Sentiment ===
+        st.subheader("📰 News & Sentiment")
+        sentiment_cols = st.columns(2)
+        with sentiment_cols[0]:
+            st.success("👍 Positive Sentiment")
+            for s in data["evaluation_parameters"]["news_and_sentiment"]["positive_sentiment"]:
+                st.write(f"✅ {s}")
+        with sentiment_cols[1]:
+            st.error("👎 Negative Sentiment")
+            for s in data["evaluation_parameters"]["news_and_sentiment"]["negative_sentiment"]:
+                st.write(f"❌ {s}")
+        st.info(data["evaluation_parameters"]["news_and_sentiment"]["assessment"])
+        
+        # === Row 6: Red Flags ===
+        st.subheader("🚩 Red Flags")
+        red_flag_cols = st.columns(3)
+        for i, flag in enumerate(data["evaluation_parameters"]["red_flags"]):
+            red_flag_cols[i].warning(f"**{flag['flag']}**\n{flag['details']}")
+        
+        # === Row 7: Investment Advice ===
+        st.subheader("💡 Investment Advice")
+        advice_cols = st.columns(3)
+        advice = data["investment_advice"]
+        advice_cols[0].markdown(f"**Next 1 Week**\n{advice['next_1_weeks_outlook']}")
+        advice_cols[1].markdown(f"**Next 5 Weeks**\n{advice['next_5_weeks_outlook']}")
+        advice_cols[2].markdown(f"**Next 10 Weeks**\n{advice['next_10_weeks_outlook']}")
+        
+        action_cols = st.columns(3)
+        action_cols[0].success(f"**Buy:** {advice['price_action_suggestions']['buy']}")
+        action_cols[1].info(f"**Hold:** {advice['price_action_suggestions']['hold']}")
+        action_cols[2].error(f"**Sell:** {advice['price_action_suggestions']['sell']}")
+        
+        # === Footer ===
+        st.markdown("---")
+        st.caption("Generated by AI-powered financial analysis dashboard.")
+    except json.JSONDecodeError as e:
+        st.error(f"JSON decode error: {e}")
+        st.write("Raw text was:")
+        st.text(res["output_text"])