Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+import matplotlib.pyplot as plt
+import pandas as pd
+from ecg_image_kit import digitize_ecg
+import neurokit2 as nk
+
+def process_ecg_image(image):
+    # Save uploaded image temporarily
+    image_path = "temp_ecg_image.png"
+    image.save(image_path)
+    
+    # Digitize image
+    time_series = load_and_digitize_ecg(image_path)
+    
+    # Analyze ECG
+    results = analyze_ecg(time_series, sampling_rate=250)
+    
+    # Generate plots and tables
+    plots = []
+    tables = []
+    for lead, data in results.items():
+        # Plot ECG signal with R-peaks
+        fig, ax = plt.subplots()
+        signals = data["signals"]
+        info = data["info"]
+        ax.plot(signals["ECG_Clean"], label="Clean ECG")
+        ax.plot(info["ECG_R_Peaks"], signals["ECG_Clean"][info["ECG_R_Peaks"]], "ro", label="R-peaks")
+        ax.set_title(f"{lead} ECG")
+        ax.legend()
+        plots.append(fig)
+        
+        # Create table of features
+        analysis = data["analysis"]
+        table = pd.DataFrame({
+            "Feature": ["Heart Rate (Mean)", "ECG Quality"],
+            "Value": [analysis.get("ECG_Rate_Mean", "N/A"), analysis.get("ECG_Quality_Mean", "N/A")]
+        })
+        tables.append(table)
+    
+    return plots, tables
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=process_ecg_image,
+    inputs=gr.Image(type="pil", label="Upload ECG Image"),
+    outputs=[
+        gr.Gallery(label="ECG Plots"),
+        gr.Dataframe(label="Analysis Results")
+    ],
+    title="ECG Image Analysis",
+    description="Upload a 12-lead ECG image to digitize and analyze it."
+)
+
+# Launch interface (for local testing)
+if __name__ == "__main__":
+    iface.launch()