| import gradio as gr |
| import numpy as np |
| import matplotlib |
| matplotlib.use('Agg') |
| import matplotlib.pyplot as plt |
| from sklearn.preprocessing import MinMaxScaler |
| import os |
| import spaces |
|
|
| os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' |
|
|
| CLASS_MAPPING = { |
| 0: "Normal Beat (N)", |
| 1: "Supraventricular Premature Beat (S)", |
| 2: "Premature Ventricular Contraction (V)", |
| 3: "Fusion of Ventricular and Normal Beat (F)", |
| 4: "Unclassifiable Beat (Q)" |
| } |
| CLASS_RISK = { |
| 0: "✅ Normal — No action required", |
| 1: "⚠️ Moderate — Monitor and consult physician", |
| 2: "🔴 High Risk — Immediate medical attention", |
| 3: "🔴 High Risk — Immediate medical attention", |
| 4: "❓ Unknown — Further evaluation needed" |
| } |
| COLORS = ['#2ecc71','#3498db','#e74c3c','#f39c12','#9b59b6'] |
|
|
| |
| model = None |
|
|
| @spaces.GPU |
| def predict_ecg(text_input): |
| global model |
| if model is None: |
| import tensorflow as tf |
| from tensorflow.keras import layers, models, regularizers |
| m = models.Sequential([ |
| layers.Input(shape=(187, 1)), |
| layers.Conv1D(64, 7, padding='same', activation='relu', |
| kernel_regularizer=regularizers.l2(1e-4)), |
| layers.BatchNormalization(), layers.MaxPooling1D(2), layers.Dropout(0.2), |
| layers.Conv1D(128, 5, padding='same', activation='relu', |
| kernel_regularizer=regularizers.l2(1e-4)), |
| layers.BatchNormalization(), layers.MaxPooling1D(2), layers.Dropout(0.25), |
| layers.Conv1D(256, 3, padding='same', activation='relu', |
| kernel_regularizer=regularizers.l2(1e-4)), |
| layers.BatchNormalization(), layers.MaxPooling1D(2), layers.Dropout(0.3), |
| layers.Conv1D(256, 3, padding='same', activation='relu', |
| kernel_regularizer=regularizers.l2(1e-4)), |
| layers.BatchNormalization(), layers.GlobalAveragePooling1D(), layers.Dropout(0.3), |
| layers.Dense(256, activation='relu', kernel_regularizer=regularizers.l2(1e-4)), |
| layers.BatchNormalization(), layers.Dropout(0.4), |
| layers.Dense(128, activation='relu', kernel_regularizer=regularizers.l2(1e-4)), |
| layers.Dropout(0.3), |
| layers.Dense(5, activation='softmax') |
| ], name="CNN_ECG") |
| m.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) |
| m.load_weights("cnn_weights.weights.h5") |
| model = m |
|
|
| try: |
| cleaned = text_input.replace(",", " ").replace("\n", " ").replace("\t", " ") |
| parsed = [float(x) for x in cleaned.split() if x.strip()] |
| count = len(parsed) |
|
|
| if count not in (187, 188): |
| return None, f"❌ Expected 187 or 188 values, got {count}.", "", "" |
|
|
| features = np.array(parsed[:187], dtype=np.float32) |
| true_label = int(parsed[187]) if count == 188 else None |
|
|
| scaler = MinMaxScaler() |
| signal_scaled = scaler.fit_transform(features.reshape(-1, 1)).reshape(1, 187, 1) |
| probs = model.predict(signal_scaled, verbose=0)[0] |
| pred_class = int(np.argmax(probs)) |
| confidence = float(np.max(probs)) * 100 |
|
|
| fig, axes = plt.subplots(1, 2, figsize=(14, 4)) |
| fig.patch.set_facecolor('#0e1117') |
|
|
| ax1 = axes[0] |
| ax1.set_facecolor('#1a1a2e') |
| ax1.plot(features, color='#00ff88', linewidth=1.8) |
| ax1.fill_between(range(187), features, alpha=0.15, color='#00ff88') |
| r_idx = int(np.argmax(features)) |
| ax1.axvline(r_idx, color='red', linestyle='--', alpha=0.7) |
| ax1.scatter(r_idx, features[r_idx], color='red', s=80, zorder=5) |
| ax1.set_title("ECG Signal", color='white', fontsize=13, fontweight='bold') |
| ax1.set_xlabel("Time Steps", color='#aaa') |
| ax1.set_ylabel("Amplitude", color='#aaa') |
| ax1.tick_params(colors='#aaa') |
| ax1.grid(True, alpha=0.2, color='#444') |
| for sp in ax1.spines.values(): sp.set_edgecolor('#333') |
|
|
| ax2 = axes[1] |
| ax2.set_facecolor('#1a1a2e') |
| bars = ax2.barh([CLASS_MAPPING[i] for i in range(5)], |
| probs * 100, color=COLORS, alpha=0.85) |
| for bar, val in zip(bars, probs): |
| ax2.text(val*100+0.5, bar.get_y()+bar.get_height()/2, |
| f"{val*100:.1f}%", va='center', color='white', fontsize=9) |
| ax2.set_xlabel("Probability (%)", color='#aaa') |
| ax2.set_title("Class Probabilities", color='white', fontsize=13, fontweight='bold') |
| ax2.set_xlim(0, 115) |
| ax2.tick_params(colors='#aaa') |
| ax2.grid(True, alpha=0.2, axis='x', color='#444') |
| for sp in ax2.spines.values(): sp.set_edgecolor('#333') |
|
|
| plt.tight_layout() |
|
|
| result = f"**🤖 Predicted:** {CLASS_MAPPING[pred_class]}\n\n**📊 Confidence:** {confidence:.2f}%\n\n**⚕️ Risk:** {CLASS_RISK[pred_class]}" |
| true_out = "" |
| if true_label is not None: |
| match = "✅ Correct!" if pred_class == true_label else "❌ Incorrect" |
| true_out = f"**🏷️ True Label:** {CLASS_MAPPING.get(true_label,'Unknown')} — {match}" |
| stats = f"**Signal Stats:** R-peak={features.max():.4f} @ t={r_idx} | Min={features.min():.4f} | Mean={features.mean():.4f} | Std={features.std():.4f}" |
|
|
| return fig, result, true_out, stats |
|
|
| except Exception as e: |
| return None, f"❌ Error: {str(e)}", "", "" |
|
|
| |
| with gr.Blocks(title="ECG Classification") as demo: |
| gr.Markdown(""" |
| # 🫀 ECG Arrhythmia Classification |
| **1D CNN | MIT-BIH Dataset | Accuracy: 97.25% | Macro F1: 0.8796** |
| --- |
| """) |
| with gr.Row(): |
| with gr.Column(scale=1): |
| text_input = gr.Textbox( |
| label="Paste 187 or 188 ECG values", |
| lines=8, |
| placeholder="0.5, 0.8, 0.3, 1.0 ..." |
| ) |
| predict_btn = gr.Button("▶ Run Prediction", variant="primary") |
| gr.Markdown(""" |
| | Label | Class | Risk | |
| |-------|-------|------| |
| | 0 | Normal (N) | ✅ | |
| | 1 | Supraventricular (S) | ⚠️ | |
| | 2 | Ventricular (V) | 🔴 | |
| | 3 | Fusion (F) | 🔴 | |
| | 4 | Unknown (Q) | ❓ | |
| """) |
| with gr.Column(scale=2): |
| plot_out = gr.Plot(label="ECG Signal & Probabilities") |
| result_out = gr.Markdown() |
| true_out = gr.Markdown() |
| stats_out = gr.Markdown() |
|
|
| predict_btn.click( |
| predict_ecg, |
| inputs=[text_input], |
| outputs=[plot_out, result_out, true_out, stats_out] |
| ) |
| gr.Markdown("*DEPI Final Project 2025*") |
|
|
| demo.launch(ssr_mode=False) |
