Update app.py

app.py

@@ -2,61 +2,131 @@ import gradio as gr
 import numpy as np
 import matplotlib.pyplot as plt
 import pickle
+import os
 
-# Load model and scaler
+# Load model and scaler with robust error handling
 def load_model():
-    with open("ecg_model.pkl", "rb") as f:
-        model_data = pickle.load(f)
-    return model_data["model"], model_data["scaler"]
+    try:
+        with open("ecg_model.pkl", "rb") as f:
+            model_data = pickle.load(f)
+        if isinstance(model_data, dict) and 'model' in model_data and 'scaler' in model_data:
+            return model_data['model'], model_data['scaler']
+        else:
+            raise ValueError("Model file doesn't contain expected data structure")
+    except Exception as e:
+        print(f"Error loading model: {str(e)}")
+        # Return dummy model for UI to still work
+        return None, None
 
+# Load the model
 model, scaler = load_model()
 
-def analyze_manual_entry(input_str):
-    # Parse input
+def detect_anomalies(voltage_list):
+    """Detect anomalies in voltage readings"""
+    if model is None or scaler is None:
+        return np.zeros(len(voltage_list)), np.zeros(len(voltage_list))
+    
+    try:
+        voltage_arr = np.array(voltage_list).reshape(-1, 1)
+        voltage_scaled = scaler.transform(voltage_arr)
+        preds = model.predict(voltage_scaled)
+        scores = model.decision_function(voltage_scaled)
+        return preds, scores
+    except Exception as e:
+        print(f"Error in anomaly detection: {str(e)}")
+        return np.zeros(len(voltage_list)), np.zeros(len(voltage_list))
+
+def analyze_ecg_data(input_str):
+    """Analyze manually entered ECG data"""
     try:
-        voltage_list = [float(x) for x in input_str.split(",") if x.strip()]
+        # Parse input
+        voltage_list = [float(x.strip()) for x in input_str.split(",") if x.strip()]
         if len(voltage_list) < 2:
-            return None, "Please enter at least 2 values.", ""
-    except Exception:
-        return None, "Invalid input. Please enter comma-separated numbers.", ""
-    # Detect anomalies
-    arr = np.array(voltage_list).reshape(-1, 1)
-    arr_scaled = scaler.transform(arr)
-    preds = model.predict(arr_scaled)
-    scores = model.decision_function(arr_scaled)
-    anomalies = [(i, v, s) for i, (p, v, s) in enumerate(zip(preds, voltage_list, scores)) if p == -1]
-    # Plot
-    plt.figure(figsize=(10, 4))
-    plt.plot(voltage_list, label="ECG Voltage")
-    if anomalies:
-        idxs = [a[0] for a in anomalies]
-        vals = [a[1] for a in anomalies]
-        plt.scatter(idxs, vals, color="red", label="Anomaly")
-    plt.title("ECG Anomaly Detection")
-    plt.xlabel("Sample Index")
-    plt.ylabel("Voltage")
-    plt.legend()
-    plt.tight_layout()
-    plt.savefig("ecg_manual_plot.png")
-    plt.close()
-    # Prepare output
-    if not anomalies:
-        summary = "No anomalies detected."
-        details = ""
-    else:
-        summary = f"Detected {len(anomalies)} anomalies."
-        details = "\n".join([f"Index: {idx}, Voltage: {v:.3f}, Score: {s:.3f}" for idx, v, s in anomalies])
-    return "ecg_manual_plot.png", summary, details
+            return None, "Please enter at least 2 voltage readings.", ""
+        
+        # Detect anomalies
+        preds, scores = detect_anomalies(voltage_list)
+        anomalies = [(i, voltage_list[i], scores[i]) for i in range(len(preds)) if preds[i] == -1]
+        
+        # Create visualization
+        plt.figure(figsize=(10, 5))
+        plt.plot(voltage_list, label="ECG Voltage")
+        
+        if anomalies:
+            anomaly_indices = [a[0] for a in anomalies]
+            anomaly_values = [a[1] for a in anomalies]
+            plt.scatter(anomaly_indices, anomaly_values, color="red", label="Anomaly")
+        
+        plt.title("ECG Anomaly Detection")
+        plt.xlabel("Sample Index")
+        plt.ylabel("Voltage (mV)")
+        plt.legend()
+        plt.grid(True, linestyle='--', alpha=0.7)
+        plt.tight_layout()
+        
+        # Save plot
+        plot_path = "ecg_analysis.png"
+        plt.savefig(plot_path)
+        plt.close()
+        
+        # Generate report
+        if not anomalies:
+            summary = "No anomalies detected in the ECG data."
+            details = "All voltage readings appear normal."
+        else:
+            summary = f"Detected {len(anomalies)} anomalies in the ECG data."
+            details = "\n".join([
+                f"Index {idx}: Voltage={val:.3f}mV, Anomaly Score={score:.3f}"
+                for idx, val, score in anomalies
+            ])
+            details += "\n\nPossible causes of anomalies:"
+            details += "\n- Voltage readings below 2.8mV may indicate conduction issues"
+            details += "\n- Voltage readings above 3.3mV may indicate hyperexcitation"
+            details += "\n- Irregular patterns may indicate arrhythmia"
+        
+        return plot_path, summary, details
+    
+    except Exception as e:
+        return None, f"Error analyzing data: {str(e)}", ""
 
+# Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🫀 ECG Anomaly Detection (Manual Entry)")
-    gr.Markdown("Enter comma-separated ECG voltages to detect anomalies.")
-    manual_input = gr.Textbox(label="ECG Voltages (comma-separated)", placeholder="e.g., 3.01,3.02,2.95,4.2")
-    manual_plot = gr.Image(label="Anomaly Plot")
-    manual_summary = gr.Textbox(label="Summary")
-    manual_details = gr.Textbox(label="Anomaly Details", lines=5)
-    manual_btn = gr.Button("Analyze")
-    manual_btn.click(analyze_manual_entry, inputs=manual_input, outputs=[manual_plot, manual_summary, manual_details])
+    gr.Markdown("# 🫀 ECG Anomaly Detection")
+    gr.Markdown("Enter comma-separated ECG voltage readings to detect anomalies.")
+    
+    with gr.Row():
+        with gr.Column():
+            input_box = gr.Textbox(
+                label="ECG Voltages (comma-separated)",
+                placeholder="e.g., 3.01,3.02,2.95,4.2,3.0,2.9,2.8,4.1,3.05,3.1",
+                lines=3
+            )
+            analyze_btn = gr.Button("Analyze ECG Data", variant="primary")
+            
+        with gr.Column():
+            plot_output = gr.Image(label="ECG Analysis")
+            summary_output = gr.Textbox(label="Summary")
+            details_output = gr.Textbox(label="Detailed Findings", lines=8)
+    
+    # Example data from your CSV
+    gr.Markdown("### Example Data")
+    gr.Markdown("Click to use sample data from Person_3:")
+    example_btn = gr.Button("Use Sample Data")
+    
+    # Sample data from your CSV (Person_3)
+    sample_data = "3.100556,3.098563,3.103323,3.107478,3.100193,3.090881,2.809517,3.106659,3.106299,3.099496,3.095472,3.090603,3.105606,3.090809,3.101076,3.090684,2.835392,3.104057,3.105907,3.091121"
+    
+    # Connect components
+    analyze_btn.click(
+        analyze_ecg_data,
+        inputs=input_box,
+        outputs=[plot_output, summary_output, details_output]
+    )
+    
+    example_btn.click(
+        lambda: sample_data,
+        outputs=input_box
+    )
 
 if __name__ == "__main__":
     demo.launch()