Update app.py

app.py

@@ -1,3 +1,236 @@
+# import streamlit as st
+# import pandas as pd
+# import numpy as np
+# import plotly.express as px
+# from datetime import datetime, timedelta
+# from io import StringIO
+# import os
+# import json
+
+# # Debug: Verify file paths
+# st.write("Debug: Checking file paths...")
+# files_to_check = ["new_best_improved_model.pth", "scaler.pkl", "feature_names.json", "model_config.json"]
+# for file in files_to_check:
+#     st.write(f"{file}: {'Found' if os.path.exists(file) else 'Missing'}")
+
+# try:
+#     from inference import load_model_and_artifacts, predict
+# except Exception as e:
+#     st.error(f"Error importing inference: {str(e)}")
+#     st.stop()
+
+# st.title("Store Sales Time Series Forecasting")
+# st.markdown("Forecast 13-week store sales using an LSTM model trained on Kaggle Store Sales data.")
+
+# # Load model and artifacts
+# try:
+#     st.write("Debug: Loading model and artifacts...")
+#     model, scaler, feature_names, config = load_model_and_artifacts()
+#     st.success("Model and artifacts loaded successfully")
+# except Exception as e:
+#     st.error(f"Error loading model or artifacts: {str(e)}")
+#     st.stop()
+
+# # Display model metrics
+# st.header("Model Performance Metrics")
+# metrics = {
+#     "MAE": 710.75,
+#     "RMSE": 1108.36,
+#     "MAPE": 7.16,
+#     "R2": 0.8633
+# }
+# st.markdown(f"""
+# - **MAE**: ${metrics['MAE']:.2f}  
+# - **RMSE**: ${metrics['RMSE']:.2f}  
+# - **MAPE**: {metrics['MAPE']:.2f}%  
+# - **R² Score**: {metrics['R2']:.4f}
+# """)
+
+# # Model architecture summary
+# st.header("Model Architecture")
+# st.markdown(f"""
+# - **Input Size**: {config['input_size']} features  
+# - **Hidden Size**: {config['hidden_size']}  
+# - **Number of Layers**: {config['num_layers']}  
+# - **Forecast Horizon**: {config['forecast_horizon']} weeks  
+# - **Dropout**: {config['dropout']}  
+# - **Attention**: {config['has_attention']}  
+# - **Input Projection**: {config['has_input_projection']}  
+# - **Parameters**: 227,441
+# """)
+
+# # Synthetic data generation
+# st.header("Generate Synthetic Test Data")
+# st.markdown("Create a sample dataset with 21 timesteps matching the training data distribution (sales ~$3,000–19,000).")
+# if st.button("Generate Synthetic Data"):
+#     np.random.seed(42)
+#     sequence_length = 21
+#     n_features = len(feature_names)
+#     synthetic_data = np.zeros((sequence_length, n_features))
+    
+#     # Generate features based on training data characteristics
+#     for i, feature in enumerate(feature_names):
+#         if feature == "sales":
+#             synthetic_data[:, i] = np.random.normal(8954.97, 3307.49, sequence_length)  # Mean, std from verbose
+#         elif feature == "onpromotion":
+#             synthetic_data[:, i] = np.random.choice([0, 1], sequence_length, p=[0.8, 0.2])
+#         elif feature in ["dayofweek_sin", "dayofweek_cos"]:
+#             synthetic_data[:, i] = np.sin(np.linspace(0, 2 * np.pi, sequence_length)) if "sin" in feature else np.cos(np.linspace(0, 2 * np.pi, sequence_length))
+#         elif feature in ["month_sin", "month_cos"]:
+#             synthetic_data[:, i] = np.sin(np.linspace(0, 2 * np.pi * 12 / sequence_length, sequence_length)) if "sin" in feature else np.cos(np.linspace(0, 2 * np.pi * 12 / sequence_length, sequence_length))
+#         elif feature == "trend":
+#             synthetic_data[:, i] = np.linspace(0, sequence_length, sequence_length)
+#         elif feature == "is_weekend":
+#             synthetic_data[:, i] = np.random.choice([0, 1], sequence_length, p=[0.7, 0.3])
+#         elif feature == "quarter":
+#             synthetic_data[:, i] = np.random.choice([1, 2, 3, 4], sequence_length)
+#         elif "lag" in feature:
+#             lag = int(feature.split('_')[-1])
+#             synthetic_data[:, i] = np.roll(synthetic_data[:, 0], lag)
+#             if lag > 0:
+#                 synthetic_data[:lag, i] = synthetic_data[:lag, 0]
+#         elif "ma" in feature:
+#             window = int(feature.split('_')[-1])
+#             synthetic_data[:, i] = pd.Series(synthetic_data[:, 0]).rolling(window=window, min_periods=1).mean().values
+#         elif "ratio" in feature:
+#             window = int(feature.split('_')[-1])
+#             ma = pd.Series(synthetic_data[:, 0]).rolling(window=window, min_periods=1).mean().values
+#             synthetic_data[:, i] = synthetic_data[:, 0] / (ma + 1e-8)
+#         elif "promo" in feature:
+#             synthetic_data[:, i] = np.random.choice([0, 1], sequence_length, p=[0.8, 0.2])
+#         elif feature == "dcoilwtico":
+#             synthetic_data[:, i] = np.random.normal(80, 10, sequence_length)
+#         elif feature == "is_holiday":
+#             synthetic_data[:, i] = np.random.choice([0, 1], sequence_length, p=[0.9, 0.1])
+    
+#     # Create DataFrame with dates
+#     synthetic_df = pd.DataFrame(synthetic_data, columns=feature_names)
+#     end_date = datetime.now().date()
+#     dates = [end_date - timedelta(days=x) for x in range(sequence_length-1, -1, -1)]
+#     synthetic_df['Date'] = dates
+    
+#     # Store in session state
+#     st.session_state["synthetic_df"] = synthetic_df
+    
+#     st.subheader("Synthetic Data Preview")
+#     st.dataframe(synthetic_df.head())
+    
+#     # Download synthetic data
+#     csv_buffer = StringIO()
+#     synthetic_df.to_csv(csv_buffer, index=False)
+#     st.download_button(
+#         label="Download Synthetic Data CSV",
+#         data=csv_buffer.getvalue(),
+#         file_name="synthetic_sales_data.csv",
+#         mime="text/csv"
+#     )
+    
+#     # Generate forecast
+#     try:
+#         sequences = synthetic_df[feature_names].values.reshape(1, sequence_length, n_features)
+#         sequences_scaled = scaler.transform(sequences.reshape(-1, n_features)).reshape(1, sequence_length, n_features)
+#         predictions, uncertainties = predict(model, scaler, sequences_scaled)
+        
+#         # Validate output shapes
+#         if predictions.shape != (1, 13) or uncertainties.shape != (1, 13):
+#             raise ValueError(f"Expected predictions and uncertainties of shape (1, 13), got {predictions.shape} and {uncertainties.shape}")
+        
+#         # Create forecast DataFrame
+#         forecast_dates = [end_date + timedelta(days=x*7) for x in range(1, 14)]
+#         forecast_df = pd.DataFrame({
+#             'Date': forecast_dates,
+#             'Predicted Sales ($)': predictions[0],
+#             'Uncertainty ($)': uncertainties[0]
+#         })
+        
+#         st.subheader("13-Week Forecast")
+#         st.dataframe(forecast_df)
+        
+#         # Plot forecast
+#         fig = px.line(forecast_df, x='Date', y='Predicted Sales ($)', title='13-Week Sales Forecast')
+#         fig.add_scatter(
+#             x=forecast_df['Date'],
+#             y=forecast_df['Predicted Sales ($)'] + forecast_df['Uncertainty ($)'],
+#             mode='lines', name='Upper Bound', line=dict(dash='dash', color='green')
+#         )
+#         fig.add_scatter(
+#             x=forecast_df['Date'],
+#             y=forecast_df['Predicted Sales ($)'] - forecast_df['Uncertainty ($)'],
+#             mode='lines', name='Lower Bound', line=dict(dash='dash', color='green'),
+#             fill='tonexty', fillcolor='rgba(0, 255, 0, 0.1)'
+#         )
+#         st.plotly_chart(fig)
+        
+#         # Download forecast
+#         csv_buffer = StringIO()
+#         forecast_df.to_csv(csv_buffer, index=False)
+#         st.download_button(
+#             label="Download Forecast CSV",
+#             data=csv_buffer.getvalue(),
+#             file_name="forecast_results.csv",
+#             mime="text/csv"
+#         )
+#     except Exception as e:
+#         st.error(f"Error generating forecast: {str(e)}")
+
+# # CSV upload for custom predictions
+# st.header("Upload Custom Data")
+# st.markdown("Upload a CSV with 21 timesteps and 20 features matching the feature names and format of the synthetic data.")
+# uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+# if uploaded_file is not None:
+#     try:
+#         data = pd.read_csv(uploaded_file)
+#         if set(feature_names).issubset(data.columns) and len(data) == 21:
+#             sequences = data[feature_names].values.reshape(1, 21, len(feature_names))
+#             sequences_scaled = scaler.transform(sequences.reshape(-1, len(feature_names))).reshape(1, 21, len(feature_names))
+#             predictions, uncertainties = predict(model, scaler, sequences_scaled)
+            
+#             # Validate output shapes
+#             if predictions.shape != (1, 13) or uncertainties.shape != (1, 13):
+#                 raise ValueError(f"Expected predictions and uncertainties of shape (1, 13), got {predictions.shape} and {uncertainties.shape}")
+            
+#             # Create forecast DataFrame
+#             forecast_df = pd.DataFrame({
+#                 'Week': range(1, 14),
+#                 'Predicted Sales ($)': predictions[0],
+#                 'Uncertainty ($)': uncertainties[0]
+#             })
+            
+#             st.subheader("13-Week Forecast")
+#             st.dataframe(forecast_df)
+            
+#             # Plot forecast
+#             fig = px.line(forecast_df, x='Week', y='Predicted Sales ($)', title='13-Week Sales Forecast')
+#             fig.add_scatter(
+#                 x=forecast_df['Week'],
+#                 y=forecast_df['Predicted Sales ($)'] + forecast_df['Uncertainty ($)'],
+#                 mode='lines', name='Upper Bound', line=dict(dash='dash', color='green')
+#             )
+#             fig.add_scatter(
+#                 x=forecast_df['Week'],
+#                 y=forecast_df['Predicted Sales ($)'] - forecast_df['Uncertainty ($)'],
+#                 mode='lines', name='Lower Bound', line=dict(dash='dash', color='green'),
+#                 fill='tonexty', fillcolor='rgba(0, 255, 0, 0.1)'
+#             )
+#             st.plotly_chart(fig)
+            
+#             # Download forecast
+#             csv_buffer = StringIO()
+#             forecast_df.to_csv(csv_buffer, index=False)
+#             st.download_button(
+#                 label="Download Forecast CSV",
+#                 data=csv_buffer.getvalue(),
+#                 file_name="custom_forecast_results.csv",
+#                 mime="text/csv"
+#             )
+#         else:
+#             st.error(f"Invalid CSV. Expected 21 rows and columns including: {', '.join(feature_names)}")
+#     except Exception as e:
+#         st.error(f"Error processing CSV or generating forecast: {str(e)}")
+
+
+
 import streamlit as st
 import pandas as pd
 import numpy as np
@@ -14,7 +247,7 @@ for file in files_to_check:
     st.write(f"{file}: {'Found' if os.path.exists(file) else 'Missing'}")
 
 try:
-    from inference import load_model_and_artifacts, predict
+    from inference import load_model_and_artifacts, predict, derive_features
 except Exception as e:
     st.error(f"Error importing inference: {str(e)}")
     st.stop()
@@ -113,11 +346,11 @@ if st.button("Generate Synthetic Data"):
     st.session_state["synthetic_df"] = synthetic_df
     
     st.subheader("Synthetic Data Preview")
-    st.dataframe(synthetic_df.head())
+    st.dataframe(synthetic_df[["Date", "sales", "onpromotion", "dcoilwtico", "is_holiday"]].head())
     
     # Download synthetic data
     csv_buffer = StringIO()
-    synthetic_df.to_csv(csv_buffer, index=False)
+    synthetic_df[["date", "sales", "onpromotion", "dcoilwtico", "is_holiday"]].rename(columns={"Date": "date"}).to_csv(csv_buffer, index=False)
     st.download_button(
         label="Download Synthetic Data CSV",
         data=csv_buffer.getvalue(),
@@ -173,16 +406,46 @@ if st.button("Generate Synthetic Data"):
     except Exception as e:
         st.error(f"Error generating forecast: {str(e)}")
 
-# CSV upload for custom predictions
+# Sample CSV for user guidance
 st.header("Upload Custom Data")
-st.markdown("Upload a CSV with 21 timesteps and 20 features matching the feature names and format of the synthetic data.")
+st.markdown("""
+Upload a CSV with 21 timesteps containing the following columns:
+- **date**: Date in YYYY-MM-DD format (e.g., 2025-06-22)
+- **sales**: Weekly sales in USD (e.g., 3000 to 19372)
+- **onpromotion**: 0 or 1 indicating if items are on promotion
+- **dcoilwtico**: Oil price (e.g., 70 to 90)
+- **is_holiday**: 0 or 1 indicating if the day is a holiday
+
+The remaining features will be derived automatically. Download a sample CSV below to see the expected format.
+""")
+
+# Generate sample CSV
+sample_data = pd.DataFrame({
+    "date": ["2025-06-22", "2025-06-15", "2025-06-08"],
+    "sales": [8954.97, 9500.00, 8000.00],
+    "onpromotion": [0, 1, 0],
+    "dcoilwtico": [80.0, 82.5, 78.0],
+    "is_holiday": [0, 0, 1]
+})
+csv_buffer = StringIO()
+sample_data.to_csv(csv_buffer, index=False)
+st.download_button(
+    label="Download Sample CSV",
+    data=csv_buffer.getvalue(),
+    file_name="sample_input.csv",
+    mime="text/csv"
+)
+
+# CSV upload for custom predictions
 uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
 
 if uploaded_file is not None:
     try:
         data = pd.read_csv(uploaded_file)
-        if set(feature_names).issubset(data.columns) and len(data) == 21:
-            sequences = data[feature_names].values.reshape(1, 21, len(feature_names))
+        required_columns = ["date", "sales", "onpromotion", "dcoilwtico", "is_holiday"]
+        if set(required_columns).issubset(data.columns) and len(data) == 21:
+            # Derive full feature set
+            sequences = derive_features(data, feature_names, sequence_length=21)
             sequences_scaled = scaler.transform(sequences.reshape(-1, len(feature_names))).reshape(1, 21, len(feature_names))
             predictions, uncertainties = predict(model, scaler, sequences_scaled)
             
@@ -191,8 +454,10 @@ if uploaded_file is not None:
                 raise ValueError(f"Expected predictions and uncertainties of shape (1, 13), got {predictions.shape} and {uncertainties.shape}")
             
             # Create forecast DataFrame
+            end_date = pd.to_datetime(data["date"].iloc[0]).date()
+            forecast_dates = [end_date + timedelta(days=x*7) for x in range(1, 14)]
             forecast_df = pd.DataFrame({
-                'Week': range(1, 14),
+                'Date': forecast_dates,
                 'Predicted Sales ($)': predictions[0],
                 'Uncertainty ($)': uncertainties[0]
             })
@@ -201,14 +466,14 @@ if uploaded_file is not None:
             st.dataframe(forecast_df)
             
             # Plot forecast
-            fig = px.line(forecast_df, x='Week', y='Predicted Sales ($)', title='13-Week Sales Forecast')
+            fig = px.line(forecast_df, x='Date', y='Predicted Sales ($)', title='13-Week Sales Forecast')
             fig.add_scatter(
-                x=forecast_df['Week'],
+                x=forecast_df['Date'],
                 y=forecast_df['Predicted Sales ($)'] + forecast_df['Uncertainty ($)'],
                 mode='lines', name='Upper Bound', line=dict(dash='dash', color='green')
             )
             fig.add_scatter(
-                x=forecast_df['Week'],
+                x=forecast_df['Date'],
                 y=forecast_df['Predicted Sales ($)'] - forecast_df['Uncertainty ($)'],
                 mode='lines', name='Lower Bound', line=dict(dash='dash', color='green'),
                 fill='tonexty', fillcolor='rgba(0, 255, 0, 0.1)'
@@ -225,6 +490,6 @@ if uploaded_file is not None:
                 mime="text/csv"
             )
         else:
-            st.error(f"Invalid CSV. Expected 21 rows and columns including: {', '.join(feature_names)}")
+            st.error(f"Invalid CSV. Expected 21 rows and columns: {', '.join(required_columns)}")
     except Exception as e:
-        st.error(f"Error processing CSV or generating forecast: {str(e)}")
+        st.error(f"Error processing CSV or generating forecast: {str(e)}")