Create app.py

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+from ucimlrepo import fetch_ucirepo
+
+# Load the trained model
+model = joblib.load("model.pkl")
+
+# Define feature columns
+FEATURE_COLUMNS = ['radius1', 'texture1', 'perimeter1', 'area1', 'smoothness1',
+       'compactness1', 'concavity1', 'concave_points1', 'symmetry1',
+       'fractal_dimension1', 'radius2', 'texture2', 'perimeter2', 'area2',
+       'smoothness2', 'compactness2', 'concavity2', 'concave_points2',
+       'symmetry2', 'fractal_dimension2', 'radius3', 'texture3', 'perimeter3',
+       'area3', 'smoothness3', 'compactness3', 'concavity3', 'concave_points3',
+       'symmetry3', 'fractal_dimension3']
+
+# Prediction function
+def predict(*features):
+    input_data = pd.DataFrame([features], columns=FEATURE_COLUMNS)
+    prediction = model.predict(input_data)
+    return "Malignant" if prediction[0] == 1 else "Benign"
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[gr.Number(label=col) for col in FEATURE_COLUMNS],
+    outputs="text",
+    title="Breast Cancer Diagnosis Predictor",
+    description="Enter the values of the features to predict the diagnosis."
+)
+
+if __name__ == "__main__":
+    iface.launch()