| import os |
| os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" |
| os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0" |
|
|
| import numpy as np |
| import gradio as gr |
| import joblib |
| import spaces |
| from tensorflow.keras.models import load_model |
|
|
| |
| |
| |
| knn_model = joblib.load("knn_model.pkl") |
| svm_model = joblib.load("rbf_svm_model.pkl") |
| ann_model = load_model("ann_model.keras") |
|
|
| scaler_rfe = joblib.load("scaler_rfe.pkl") |
| selected_features = joblib.load("selected_features.pkl") |
|
|
| |
| |
| |
| gender_map = {"Female": 0, "Male": 1} |
| binary_map = {"No": 0, "Yes": 1} |
| ordinal_map = {"Low": 0, "Medium": 1, "High": 2} |
| alcohol_map = {"None": 0, "Low": 1, "Medium": 2, "High": 3} |
|
|
| binary_features = {'smoking', 'family_hd', 'diabetes', 'high_bp', 'low_hdl', 'high_ldl'} |
| ordinal_features = {'exercise', 'stress', 'sugar'} |
|
|
| def encode_input_value(feature, value): |
| if feature == 'gender': return gender_map[value] |
| if feature in binary_features: return binary_map[value] |
| if feature in ordinal_features: return ordinal_map[value] |
| if feature == 'alcohol': return alcohol_map[value] |
| return float(value) |
|
|
| |
| |
| |
| |
| |
| @spaces.GPU |
| def dummy_startup(): |
| pass |
|
|
| |
| |
| |
| def predict_heart_disease(model_choice, *feature_values): |
| try: |
| encoded_inputs = [encode_input_value(f, v) for f, v in zip(selected_features, feature_values)] |
| X_input = np.array([encoded_inputs], dtype=float) |
| X_scaled = scaler_rfe.transform(X_input) |
|
|
| if model_choice == "KNN": |
| y_pred = knn_model.predict(X_scaled)[0] |
| y_prob = knn_model.predict_proba(X_scaled)[0][1] |
| elif model_choice == "SVM": |
| y_pred = svm_model.predict(X_scaled)[0] |
| if hasattr(svm_model, "predict_proba"): |
| y_prob = svm_model.predict_proba(X_scaled)[0][1] |
| else: |
| score = svm_model.decision_function(X_scaled)[0] |
| y_prob = 1 / (1 + np.exp(-score)) |
| else: |
| y_prob = float(ann_model.predict(X_scaled, verbose=0)[0][0]) |
| y_pred = int(y_prob >= 0.5) |
|
|
| status = "Yes" if int(y_pred) == 1 else "No" |
| probability_text = f"{y_prob:.4f} ({y_prob * 100:.2f}%)" |
|
|
| return status, probability_text, model_choice |
| |
| except Exception as e: |
| |
| return f"Error: {str(e)}", "Error", model_choice |
|
|
| |
| |
| |
| feature_labels = { |
| 'age': 'Age', 'gender': 'Gender', 'blood_pressure': 'Blood Pressure', |
| 'cholesterol': 'Cholesterol Level', 'exercise': 'Exercise Habits', |
| 'smoking': 'Smoking', 'family_hd': 'Family Heart Disease', 'diabetes': 'Diabetes', |
| 'bmi': 'BMI', 'high_bp': 'High Blood Pressure', 'low_hdl': 'Low HDL Cholesterol', |
| 'high_ldl': 'High LDL Cholesterol', 'alcohol': 'Alcohol Consumption', |
| 'stress': 'Stress Level', 'sleep_hours': 'Sleep Hours', 'sugar': 'Sugar Consumption', |
| 'triglyceride': 'Triglyceride Level', 'fasting_bs': 'Fasting Blood Sugar', |
| 'crp': 'CRP Level', 'homocysteine': 'Homocysteine Level' |
| } |
|
|
| def create_feature_input(feature): |
| label = feature_labels.get(feature, feature) |
| if feature == 'gender': return gr.Dropdown(["Female", "Male"], label=label, value="Female") |
| if feature in binary_features: return gr.Dropdown(["No", "Yes"], label=label, value="No") |
| if feature in ordinal_features: return gr.Dropdown(["Low", "Medium", "High"], label=label, value="Medium") |
| if feature == 'alcohol': return gr.Dropdown(["None", "Low", "Medium", "High"], label=label, value="None") |
| return gr.Number(label=label, value=0.0) |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown("<h2 style='text-align:center'>💓 Heart Disease Prediction 💓</h2>") |
| |
| with gr.Row(): |
| model_input = gr.Dropdown(["ANN", "SVM", "KNN"], label="Choose Model", value="ANN") |
|
|
| feature_inputs = [] |
| for start in range(0, len(selected_features), 5): |
| with gr.Row(): |
| for feature in selected_features[start:start + 5]: |
| feature_inputs.append(create_feature_input(feature)) |
|
|
| predict_button = gr.Button("Predict 💡", variant="primary") |
|
|
| with gr.Row(): |
| status_output = gr.Textbox(label="Heart Disease Status") |
| prob_output = gr.Textbox(label="Prediction Probability") |
| model_output = gr.Textbox(label="Model Selected") |
|
|
| predict_button.click( |
| fn=predict_heart_disease, |
| inputs=[model_input] + feature_inputs, |
| outputs=[status_output, prob_output, model_output] |
| ) |
|
|
| demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False) |
