initial commit

.gitignore

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+.venv
+.ropeproject

main.py

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+import gradio as gr
+import numpy as np
+import xgboost as xgb
+from ucimlrepo import fetch_ucirepo
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import StandardScaler
+from imblearn.over_sampling import SMOTE
+import os
+
+# Paths for saving/loading the model
+MODEL_PATH = "heart_disease_model.json"
+
+# Load and preprocess the data
+heart_disease = fetch_ucirepo(id=45)
+X = heart_disease.data.features
+y = np.ravel(heart_disease.data.targets)
+
+imputer = SimpleImputer(strategy="mean")
+X = imputer.fit_transform(X)
+scaler = StandardScaler()
+X = scaler.fit_transform(X)
+smote = SMOTE(random_state=42)
+X_resampled, y_resampled = smote.fit_resample(X, y)
+
+# Train or load the model
+if os.path.exists(MODEL_PATH):
+    # Load pre-trained model
+    model = xgb.Booster()
+    model.load_model(MODEL_PATH)
+else:
+    # Train the model
+    dtrain = xgb.DMatrix(X_resampled, label=y_resampled)
+    param_grid = {
+        "objective": "multi:softmax",
+        "num_class": len(np.unique(y_resampled)),
+        "eval_metric": "mlogloss",
+        "learning_rate": 0.1,
+        "max_depth": 5,
+        "subsample": 0.8,
+        "colsample_bytree": 0.8,
+    }
+    model = xgb.train(params=param_grid, dtrain=dtrain, num_boost_round=100)
+    # Save the model
+    model.save_model(MODEL_PATH)
+
+
+# Define prediction function
+def predict(
+    age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal
+):
+    # Convert string values to numeric where needed
+    sex = int(sex)  # Convert '1: Male' -> 1
+    cp = int(cp)
+    fbs = int(fbs)
+    restecg = int(restecg)
+    exang = int(exang)
+    slope = int(slope)
+    thal = int(thal)
+
+    # Combine inputs into a single feature list
+    features = np.array(
+        [
+            age,
+            sex,
+            cp,
+            trestbps,
+            chol,
+            fbs,
+            restecg,
+            thalach,
+            exang,
+            oldpeak,
+            slope,
+            ca,
+            thal,
+        ]
+    ).reshape(1, -1)
+
+    # Preprocess the inputs
+    features = scaler.transform(imputer.transform(features))
+
+    # Predict using the trained model
+    dmatrix = xgb.DMatrix(features)
+    prediction = model.predict(dmatrix)
+    return int(prediction[0])
+
+
+# Update Gradio interface to return numeric values
+feature_inputs = [
+    gr.Number(label="Age (years)"),
+    gr.Radio(label="Sex", choices=["0", "1"], type="value"),  # Male: 1, Female: 0
+    gr.Radio(label="Chest Pain Type (cp)", choices=["0", "1", "2", "3"], type="value"),
+    gr.Number(label="Resting Blood Pressure (mm Hg)"),
+    gr.Number(label="Serum Cholestoral (mg/dl)"),
+    gr.Radio(
+        label="Fasting Blood Sugar > 120 mg/dl (fbs)", choices=["0", "1"], type="value"
+    ),
+    gr.Radio(
+        label="Resting ECG Results (restecg)", choices=["0", "1", "2"], type="value"
+    ),
+    gr.Number(label="Maximum Heart Rate Achieved (thalach)"),
+    gr.Radio(label="Exercise Induced Angina (exang)", choices=["0", "1"], type="value"),
+    gr.Number(label="ST Depression Induced by Exercise (oldpeak)"),
+    gr.Radio(
+        label="Slope of the Peak Exercise ST Segment (slope)",
+        choices=["0", "1", "2"],
+        type="value",
+    ),
+    gr.Number(label="Number of Major Vessels Colored by Fluoroscopy (ca)"),
+    gr.Radio(label="Thalassemia (thal)", choices=["0", "1", "2", "3"], type="value"),
+]
+
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=feature_inputs,
+    outputs="label",
+    title="Heart Disease Prediction",
+    description=(
+        "Predicts heart disease based on patient information. "
+        "Provide the required features to get a diagnosis prediction."
+    ),
+)
+
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+certifi==2024.12.14
+contourpy==1.3.1
+cycler==0.12.1
+fonttools==4.55.3
+imbalanced-learn==0.13.0
+joblib==1.4.2
+kiwisolver==1.4.8
+matplotlib==3.10.0
+numpy==2.2.1
+nvidia-nccl-cu12==2.23.4
+packaging==24.2
+pandas==2.2.3
+pillow==11.0.0
+pyparsing==3.2.0
+python-dateutil==2.9.0.post0
+pytz==2024.2
+scikit-learn==1.6.0
+scipy==1.14.1
+six==1.17.0
+sklearn-compat==0.1.3
+threadpoolctl==3.5.0
+tzdata==2024.2
+ucimlrepo==0.0.7
+xgboost==2.1.3