Update app.py

app.py

@@ -28,19 +28,49 @@ ALPHA_VANTAGE_API_KEY = "JK0DVDNTEYBTBP5L"
 # Custom CSS
 st.markdown("""
 <style>
-    .reportview-container .main .block-container {
-        max-width: 1200px;
+    .reportview-container {
+        background: linear-gradient(to right, #1e3c72, #2a5298);
+    }
+    .main .block-container {
+        max-width: 95%;
         padding-top: 2rem;
         padding-bottom: 2rem;
     }
-    .stAlert > div {
-        padding-top: 1rem;
-        padding-bottom: 1rem;
+    h1, h2, h3 {
+        color: #f0f2f6;
     }
     .stButton>button {
-        width: 100%;
+        color: #ffffff;
+        background-color: #4CAF50;
+        border-radius: 5px;
+        border: none;
+        padding: 10px 24px;
+        transition: all 0.3s ease 0s;
+    }
+    .stButton>button:hover {
+        background-color: #45a049;
+    }
+    .stTextInput>div>div>input {
+        color: #1e3c72;
+    }
+    .stDateInput>div>div>input {
+        color: #1e3c72;
+    }
+    .stPlotlyChart {
+        background-color: rgba(255, 255, 255, 0.1);
+        border-radius: 5px;
+        padding: 10px;
+    }
+    .css-1d391kg {
+        background-color: rgba(255, 255, 255, 0.05);
+    }
+    .stDataFrame {
+        background-color: rgba(255, 255, 255, 0.1);
+        border-radius: 5px;
+        padding: 10px;
     }
 </style>
+
 """, unsafe_allow_html=True)
 
 @st.cache_data
@@ -475,67 +505,73 @@ def analyze_stock(ticker, start_date, end_date, use_ai_assistant):
             return None
 
 def main():
-    st.title("Advanced Stock Analysis App")
+    st.title("🚀 Advanced Stock Analysis App")
     st.markdown("Enter a stock ticker and date range to perform comprehensive stock analysis.")
 
-    col1, col2, col3 = st.columns(3)
+    col1, col2, col3, col4 = st.columns([2,2,2,1])
     with col1:
         ticker = st.text_input("Stock Ticker", value="MSFT")
     with col2:
         start_date = st.date_input("Start Date", value=datetime(2015, 1, 1))
     with col3:
         end_date = st.date_input("End Date", value=datetime.now())
+    with col4:
+        use_ai_assistant = st.checkbox("Use AI Assistant")
 
-    use_ai_assistant = st.checkbox("Use AI Assistant for Analysis")
-
-    if st.button("Analyze Stock"):
+    if st.button("Analyze Stock", key="analyze_button"):
         results = analyze_stock(ticker, start_date, end_date, use_ai_assistant)
         
         if results:
-            st.subheader("Fair Price Analysis")
+            st.header("📊 Fair Price Analysis")
             col1, col2, col3 = st.columns(3)
             with col1:
                 st.metric("Current Price", f"${results['latest_close']:.2f}")
             with col2:
+                change_pct = ((results['latest_fair_value'] - results['latest_close']) / results['latest_close'] * 100)
                 st.metric("Estimated Fair Value", f"${results['latest_fair_value']:.2f}", 
-                          f"{((results['latest_fair_value'] - results['latest_close']) / results['latest_close'] * 100):.2f}%")
+                          f"{change_pct:+.2f}%", 
+                          delta_color="normal" if change_pct >= 0 else "inverse")
             with col3:
                 st.metric("R-squared Score", f"{results['r2']:.4f}")
 
             st.metric("Price Prediction Range", 
                       f"${results['latest_lower_bound']:.2f} to ${results['latest_upper_bound']:.2f}")
 
-            st.subheader("Fair Price Prediction")
+            st.subheader("📈 Fair Price Prediction")
             st.plotly_chart(results['fig'], use_container_width=True)
 
             col1, col2 = st.columns(2)
             with col1:
-                st.subheader("SHAP Feature Importance")
+                st.subheader("🔍 SHAP Feature Importance")
                 st.pyplot(results['shap_fig'])
             with col2:
-                st.subheader("Top 10 Important Features")
-                st.dataframe(results['feature_importance_df'].head(10))
+                st.subheader("🏆 Top 10 Important Features")
+                st.dataframe(results['feature_importance_df'].head(10), height=400)
 
-            st.subheader("Monthly Seasonality")
+            st.subheader("📅 Monthly Seasonality")
             st.plotly_chart(results['seasonality_fig'], use_container_width=True)
 
             current_month = datetime.now().month
             next_month = (current_month % 12) + 1
             col1, col2 = st.columns(2)
             with col1:
+                current_change = results['seasonality'].loc[current_month, 'Mean Change%']*100
                 st.metric(f"Current month ({datetime.now().strftime('%B')})", 
-                          f"{results['seasonality'].loc[current_month, 'Mean Change%']*100:.2f}%",
-                          f"{results['seasonality'].loc[current_month, 'Positive Periods']*100:.1f}% probability of positive return")
+                          f"{current_change:.2f}%",
+                          f"{results['seasonality'].loc[current_month, 'Positive Periods']*100:.1f}% probability of positive return",
+                          delta_color="normal" if current_change >= 0 else "inverse")
             with col2:
+                next_change = results['seasonality'].loc[next_month, 'Mean Change%']*100
                 st.metric(f"Next month ({(datetime.now() + timedelta(days=31)).strftime('%B')})",
-                          f"{results['seasonality'].loc[next_month, 'Mean Change%']*100:.2f}%",
-                          f"{results['seasonality'].loc[next_month, 'Positive Periods']*100:.1f}% probability of positive return")
+                          f"{next_change:.2f}%",
+                          f"{results['seasonality'].loc[next_month, 'Positive Periods']*100:.1f}% probability of positive return",
+                          delta_color="normal" if next_change >= 0 else "inverse")
 
             if results['gpt_analysis'] != "AI assistant analysis not requested.":
-                st.subheader("AI Assistant Analysis")
+                st.subheader("🤖 AI Assistant Analysis")
                 st.text_area("Analysis", value=results['gpt_analysis'], height=300)
 
-            st.subheader("Logarithmic Stock Chart")
+            st.subheader("📉 Logarithmic Stock Chart")
             st.plotly_chart(results['log_chart'], use_container_width=True)
 
 if __name__ == "__main__":