Create app.py

app.py

@@ -0,0 +1,109 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import wfdb
+import tempfile
+import os
+from scipy.signal import resample
+import matplotlib.pyplot as plt
+# Custom activation functions
+def sin_activation(x):
+    return tf.math.sin(x)
+
+def cos_activation(x):
+    return tf.math.cos(x)
+
+# Load model with custom objects
+@st.cache_resource
+def load_model():
+    return tf.keras.models.load_model(
+        "model.keras",  # Use the .keras format instead of .h5
+        custom_objects={
+            'sin': sin_activation,
+            'cos': cos_activation,
+            'gelu': tf.keras.activations.gelu
+        }
+    )
+
+model = load_model()
+
+# AAMI class map
+class_map = {
+    0: "Normal",
+    1: "Supraventricular Ectopic (SVEB)",
+    2: "Ventricular Ectopic (VEB)",
+    3: "Fusion Beat",
+    4: "Unknown"
+}
+
+def extract_beats(record, annotation, window_size=257):
+    beats = []
+    r_locs = annotation.sample
+    signal = record.p_signal[:, 0]  # Using first channel
+    
+    for r in r_locs:
+        start = max(0, r - window_size//2)
+        end = min(len(signal), r + window_size//2 + 1)
+        
+        if end - start == window_size:
+            beat = signal[start:end]
+            beats.append(beat)
+            
+    return np.array(beats)
+
+st.title("ECG Arrhythmia Classification")
+st.write("Upload MIT-BIH record files (.dat, .hea, .atr)")
+
+uploaded_files = st.file_uploader(
+    "Choose files", 
+    type=["dat", "hea", "atr"],
+    accept_multiple_files=True
+)
+
+if uploaded_files:
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Save uploaded files
+        for f in uploaded_files:
+            file_path = os.path.join(tmpdir, f.name)
+            with open(file_path, "wb") as f_out:
+                f_out.write(f.getbuffer())
+        
+        # Find base record name
+        base_names = {os.path.splitext(f.name)[0] for f in uploaded_files}
+        common_base = list(base_names)[0]  # Get first base name
+        
+        try:
+            # Read record
+            record = wfdb.rdrecord(os.path.join(tmpdir, common_base))
+            annotation = wfdb.rdann(os.path.join(tmpdir, common_base), 'atr')
+            
+            # Process beats
+            beats = extract_beats(record, annotation)
+            if len(beats) == 0:
+                st.error("No valid beats found in the record")
+                st.stop()
+                
+            # Preprocess and predict
+            beats = beats.reshape((-1, 257, 1)).astype(np.float32)
+            predictions = model.predict(beats)
+            predicted_classes = np.argmax(predictions, axis=1)
+            
+            # Display results
+            st.subheader("Classification Results")
+            results = pd.DataFrame({
+                "Beat Index": range(len(beats)),
+                "Predicted Class": [class_map[c] for c in predicted_classes],
+                "Confidence": np.max(predictions, axis=1)
+            })
+            
+            st.dataframe(results)
+            
+            # Add visualization
+            st.subheader("Sample ECG Beat")
+            fig, ax = plt.subplots()
+            ax.plot(beats[0].flatten())
+            st.pyplot(fig)
+            
+        except Exception as e:
+            st.error(f"Error processing files: {str(e)}")