Update app.py

app.py

@@ -1,5 +1,5 @@
 import streamlit as st
-from datetime import date, datetime
+from datetime import date, datetime, timedelta
 import yfinance as yf
 import pandas as pd
 import numpy as np
@@ -35,6 +35,12 @@ st.markdown("""
     .reportview-container {
         background: #f0f2f6
     }
+    .custom-date {
+        margin-top: 1rem;
+        padding: 1rem;
+        background-color: #f8f9fa;
+        border-radius: 0.5rem;
+    }
     </style>
     """, unsafe_allow_html=True)
 
@@ -101,7 +107,6 @@ def plot_technical_analysis(data, selected_asset):
     """Create technical analysis plot."""
     fig = go.Figure()
     
-    # Add candlestick chart
     fig.add_trace(go.Candlestick(
         x=data['Date'],
         open=data['Open'],
@@ -111,7 +116,6 @@ def plot_technical_analysis(data, selected_asset):
         name='Price'
     ))
     
-    # Add moving averages
     fig.add_trace(go.Scatter(
         x=data['Date'],
         y=data['SMA_20'],
@@ -134,18 +138,80 @@ def plot_technical_analysis(data, selected_asset):
     
     return fig
 
+def plot_forecast_components(model, forecast):
+    """Create custom forecast components plot."""
+    fig = go.Figure()
+    
+    # Trend
+    fig.add_trace(go.Scatter(
+        x=forecast['ds'],
+        y=forecast['trend'],
+        name='Trend',
+        line=dict(color='blue')
+    ))
+    
+    # Yearly seasonality
+    if 'yearly' in forecast.columns:
+        fig.add_trace(go.Scatter(
+            x=forecast['ds'],
+            y=forecast['yearly'],
+            name='Yearly Seasonality',
+            line=dict(color='green')
+        ))
+    
+    # Weekly seasonality
+    if 'weekly' in forecast.columns:
+        fig.add_trace(go.Scatter(
+            x=forecast['ds'],
+            y=forecast['weekly'],
+            name='Weekly Seasonality',
+            line=dict(color='red')
+        ))
+    
+    fig.update_layout(
+        title='Forecast Components',
+        template='plotly_dark',
+        height=800,
+        showlegend=True
+    )
+    
+    return fig
+
 @st.cache_data
 def convert_df_to_csv(df):
     """Convert dataframe to CSV for download."""
     return df.to_csv(index=False).encode('utf-8')
 
+def get_specific_date_prediction(model, date_input, forecast):
+    """Get prediction for a specific date."""
+    try:
+        date_prediction = forecast[forecast['ds'] == pd.to_datetime(date_input)].iloc[0]
+        return {
+            'Predicted Value': f"${date_prediction['yhat']:.2f}",
+            'Lower Bound': f"${date_prediction['yhat_lower']:.2f}",
+            'Upper Bound': f"${date_prediction['yhat_upper']:.2f}",
+            'Trend': f"${date_prediction['trend']:.2f}"
+        }
+    except IndexError:
+        return None
+
 def main():
     st.title('📈 Advanced Stock & Cryptocurrency Forecast')
     
+    # Search bar for assets
+    search_term = st.text_input('🔍 Search for assets (e.g., "AAPL" for Apple Inc.)', '')
+    
+    # Filter assets based on search
+    filtered_assets = {
+        category: [asset for asset in assets 
+                  if search_term.upper() in asset.upper()]
+        for category, assets in ASSETS.items()
+    }
+    
     # Sidebar configuration
     st.sidebar.title("⚙️ Configuration")
-    asset_type = st.sidebar.radio("Select Asset Type", list(ASSETS.keys()))
-    selected_asset = st.sidebar.selectbox('Select Asset', ASSETS[asset_type])
+    asset_type = st.sidebar.radio("Select Asset Type", list(filtered_assets.keys()))
+    selected_asset = st.sidebar.selectbox('Select Asset', filtered_assets[asset_type])
     
     # Main content layout
     col1, col2 = st.columns(2)
@@ -156,6 +222,15 @@ def main():
     
     period = n_years * 365
     
+    # Date-specific prediction section
+    st.subheader('🎯 Get Prediction for Specific Date')
+    prediction_date = st.date_input(
+        "Select a date for prediction",
+        min_value=date.today(),
+        max_value=date.today() + timedelta(days=period),
+        value=date.today() + timedelta(days=30)
+    )
+    
     # Load and process data
     with st.spinner('Loading data...'):
         data = load_data(selected_asset)
@@ -178,6 +253,19 @@ def main():
             model, future = train_prophet_model(df_prophet, period)
             forecast = model.predict(future)
             
+            # Get specific date prediction
+            specific_prediction = get_specific_date_prediction(
+                model, 
+                prediction_date,
+                forecast
+            )
+            
+            if specific_prediction:
+                st.subheader(f"Prediction for {prediction_date}")
+                cols = st.columns(4)
+                for i, (metric, value) in enumerate(specific_prediction.items()):
+                    cols[i].metric(metric, value)
+            
             # Calculate metrics
             historical_predictions = forecast[forecast['ds'].isin(df_prophet['ds'])]
             mae = mean_absolute_error(df_prophet['y'], historical_predictions['yhat'])
@@ -197,9 +285,9 @@ def main():
             fig_forecast.update_layout(template='plotly_dark')
             st.plotly_chart(fig_forecast, use_container_width=True)
             
-            # Display components
+            # Display components using custom plotting function
             st.subheader("📊 Forecast Components")
-            fig_components = model.plot_components(forecast)
+            fig_components = plot_forecast_components(model, forecast)
             st.plotly_chart(fig_components, use_container_width=True)
             
             # Add download button
@@ -213,7 +301,7 @@ def main():
             
         except Exception as e:
             st.error(f"Error in prediction: {str(e)}")
-            st.exception(e)  # This will show the full traceback
+            st.exception(e)
 
 if __name__ == "__main__":
     main()