app

app.py

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+import streamlit as st
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+import joblib
+
+# Load the model and scaler
+def load_model(model_path, scaler_path):
+    model_state = torch.load(model_path)
+    model = LSTMPredictor(
+        input_dim=model_state['model_architecture']['input_dim'],
+        hidden_dim=model_state['model_architecture']['hidden_dim'],
+        output_dim=model_state['model_architecture']['output_dim'],
+        forecast_horizon=model_state['model_architecture']['forecast_horizon'],
+        n_layers=model_state['model_architecture']['n_layers'],
+        dropout=model_state['model_architecture']['dropout']
+    )
+    model.load_state_dict(model_state['model_state_dict'])
+    scaler = joblib.load(scaler_path)
+    return model, scaler
+
+# Prepare Streamlit interface
+st.title("Crisis Severity Prediction Model Evaluation")
+st.sidebar.title("Model Evaluation Dashboard")
+
+# Upload model and scaler files
+model_file = st.sidebar.file_uploader("Upload Trained Model", type=["pth", "pt"])
+scaler_file = st.sidebar.file_uploader("Upload Scaler File", type=["pkl"])
+
+if model_file and scaler_file:
+    # Load model and scaler
+    model, scaler = load_model(model_file, scaler_file)
+
+    # Example of how to prepare test data (adjust for actual data)
+    X_test = np.array([[...]])  # Test data input
+    y_test = np.array([[...]])  # Actual target values
+    
+    # Scale the test data using the loaded scaler
+    scaled_X_test = scaler.transform(X_test)
+
+    # Convert to tensor
+    X_test_tensor = torch.FloatTensor(scaled_X_test)
+
+    # Get predictions
+    model.eval()
+    with torch.no_grad():
+        predictions = model(X_test_tensor)
+
+    # Evaluate and display metrics (using first 3 months for example)
+    y_true = y_test
+    y_pred = predictions.numpy()
+    
+    metrics = {}
+    for month in range(3):  # Assuming forecast for 3 months
+        month_metrics = {
+            'mse': mean_squared_error(y_true[:, month], y_pred[:, month]),
+            'rmse': np.sqrt(mean_squared_error(y_true[:, month], y_pred[:, month])),
+            'mae': mean_absolute_error(y_true[:, month], y_pred[:, month]),
+            'r2': r2_score(y_true[:, month], y_pred[:, month])
+        }
+        metrics[f'month_{month+1}'] = month_metrics
+
+    # Display metrics
+    st.subheader("Model Performance Metrics:")
+    for month, month_metrics in metrics.items():
+        st.write(f"{month.upper()}:")
+        for metric_name, metric_value in month_metrics.items():
+            st.write(f"{metric_name.upper()}: {metric_value:.4f}")
+
+    # Visualization (actual vs predicted)
+    fig, ax = plt.subplots(figsize=(10, 6))
+    for month in range(3):
+        ax.scatter(y_true[:, month], y_pred[:, month], alpha=0.5, label=f'Month {month+1}')
+    ax.plot([0, 5], [0, 5], 'r--')
+    ax.set_xlabel('Actual Severity Index')
+    ax.set_ylabel('Predicted Severity Index')
+    ax.set_title('Actual vs Predicted')
+    ax.legend()
+
+    st.pyplot(fig)
+
+    # Option to download plot
+    st.download_button("Download Evaluation Plot", "evaluation_plot.png")
+else:
+    st.warning("Please upload both the trained model and scaler files.")