Update app.py

app.py

@@ -175,9 +175,9 @@ if ticker_user!="":
             interval=interval,
         )
 
-    st.title("Analysis Summary")
-    st.dataframe(analysis_summary.summary)
-    query = f"{ticker_user} stock"
+    # st.title("Analysis Summary")
+    # st.dataframe(analysis_summary.summary)
+    # query = f"{ticker_user} stock"
 
     details = {
         "symbol": ticker_user,
@@ -185,20 +185,47 @@ if ticker_user!="":
         "screener": "india",
         "interval": interval,
     }
-    st.title("Details")
-    st.dataframe(details)
+    # st.title("Details")
+    # st.dataframe(details)
 
-    st.title("Oscillator Analysis")
-    st.dataframe(analysis_summary.oscillators)
+    # st.title("Oscillator Analysis")
+    # st.dataframe(analysis_summary.oscillators)
 
-    st.title("Moving Averages")
-    st.dataframe(analysis_summary.moving_averages)
+    # st.title("Moving Averages")
+    # st.dataframe(analysis_summary.moving_averages)
 
-    st.title("Summary")
-    st.dataframe(analysis_summary.summary)
+    # st.title("Summary")
+    # st.dataframe(analysis_summary.summary)
 
-    st.title("Indicators")
-    st.dataframe(analysis_summary.indicators)
+    # st.title("Indicators")
+    # st.dataframe(analysis_summary.indicators)
+
+    # Page Title
+    st.title(f"📊 TradingView Analysis: {ticker_user} ({details['Exchange']})")
+    
+    # --- Row 1: Details + Summary ---
+    col1, col2 = st.columns([1, 3])
+    with col1:
+        st.subheader("ℹ️ Details")
+        st.table(details)  # Using st.table for a concise key-value look
+    
+    with col2:
+        st.subheader("📝 Summary")
+        st.dataframe(analysis_summary.summary, use_container_width=True)
+    
+    # --- Row 2: Oscillators + Moving Averages ---
+    col3, col4 = st.columns(2)
+    with col3:
+        st.subheader("⚙️ Oscillator Analysis")
+        st.dataframe(analysis_summary.oscillators, use_container_width=True)
+    
+    with col4:
+        st.subheader("📈 Moving Averages")
+        st.dataframe(analysis_summary.moving_averages, use_container_width=True)
+    
+    # --- Row 3: Indicators ---
+    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"
@@ -243,9 +270,86 @@ if ticker_user!="":
         # message = llm_vis.invoke([hmessage])
         
         # st.write(message.content)
-        st.write(res["output_text"])
-    
-        # print("Image saved as chart-img-02.png")
+        # st.write(res["output_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.")
     else:
         st.warning(f"Failed to retrieve image. Status code: {response.status_code}")
         st.warning("Response:", response.text)