Update app.py

app.py

@@ -26,7 +26,6 @@ def load_model():
             'gelu': tf.keras.activations.gelu
         }
     )
-
 model = load_model()
 
 # AAMI class map
@@ -42,20 +41,17 @@ 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) or load record 108")
-
 record_loaded = False
 record = None
 annotation = None
@@ -83,10 +79,10 @@ if uploaded_files and not record_loaded:
             file_path = os.path.join(tmpdir, f.name)
             with open(file_path, "wb") as f_out:
                 f_out.write(f.getbuffer())
-
+        
         base_names = {os.path.splitext(f.name)[0] for f in uploaded_files}
         common_base = list(base_names)[0]
-
+        
         try:
             record = wfdb.rdrecord(os.path.join(tmpdir, common_base))
             annotation = wfdb.rdann(os.path.join(tmpdir, common_base), 'atr')
@@ -100,7 +96,7 @@ if record_loaded and record is not None and annotation is not None:
     if len(beats) == 0:
         st.error("No valid beats found in the record")
         st.stop()
-
+        
     beats = beats.reshape((-1, 257, 1)).astype(np.float32)
     predictions = model.predict(beats)
     predicted_classes = np.argmax(predictions, axis=1)
@@ -113,7 +109,29 @@ if record_loaded and record is not None and annotation is not None:
     })
     st.dataframe(results)
 
+    # Class Distribution Section
+    st.subheader("Class Distribution")
+    
+    # Get counts for all classes
+    class_indices = list(class_map.keys())
+    class_names = [class_map[i] for i in class_indices]
+    counts = [np.sum(predicted_classes == i) for i in class_indices]
+    
+    # Create distribution dataframe
+    distribution_df = pd.DataFrame({
+        "Class": class_names,
+        "Count": counts
+    })
+
+    # Display in two columns
+    col1, col2 = st.columns([1, 2])
+    with col1:
+        st.dataframe(distribution_df.style.format({'Count': '{:,}'}))
+    
+    with col2:
+        st.bar_chart(distribution_df.set_index('Class'))
+
     st.subheader("Sample ECG Beat")
     fig, ax = plt.subplots()
     ax.plot(beats[0].flatten())
-    st.pyplot(fig)
+    st.pyplot(fig)