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import pandas as pd
import numpy as np
import pickle
# ==============================
# Load Saved Model Files
# ==============================
with open("final_model.pkl", "rb") as f:
final_model = pickle.load(f)
with open("scaler.pkl", "rb") as f:
scaler = pickle.load(f)
with open("label_encoder.pkl", "rb") as f:
label_encoder = pickle.load(f)
# ==============================
# Feature Columns (Same as Training)
# ==============================
feature_columns = [
'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',
'smoothness_mean', 'compactness_mean', 'concavity_mean',
'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
'radius_se', 'texture_se', 'perimeter_se', 'area_se',
'smoothness_se', 'compactness_se', 'concavity_se',
'concave points_se', 'symmetry_se', 'fractal_dimension_se',
'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst',
'smoothness_worst', 'compactness_worst', 'concavity_worst',
'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst'
]
# ==============================
# Prediction Function
# ==============================
def predict_cancer(*inputs):
# Convert input into dataframe
input_df = pd.DataFrame([inputs], columns=feature_columns)
# Scale data
scaled_data = scaler.transform(input_df)
scaled_df = pd.DataFrame(scaled_data, columns=feature_columns)
# Feature Engineering (must match training)
scaled_df['radius_area_ratio'] = scaled_df['radius_mean'] / (scaled_df['area_mean'] + 1e-6)
scaled_df['perimeter_area_ratio'] = scaled_df['perimeter_mean'] / (scaled_df['area_mean'] + 1e-6)
scaled_df['concavity_points_product'] = (
scaled_df['concavity_mean'] * scaled_df['concave points_mean']
)
# Prediction
probabilities = final_model.predict_proba(scaled_df)[0]
class_index = np.argmax(probabilities)
predicted_label = label_encoder.inverse_transform([class_index])[0]
confidence = probabilities[class_index] * 100
diagnosis_map = {
"M": "Malignant (Cancer)",
"B": "Benign (Non-cancerous)"
}
result = diagnosis_map.get(predicted_label, predicted_label)
return result, f"{confidence:.2f}%"
# ==============================
# Create Gradio UI
# ==============================
inputs = [gr.Number(label=col, value=0.0) for col in feature_columns]
interface = gr.Interface(
fn=predict_cancer,
inputs=inputs,
outputs=[
gr.Textbox(label="Predicted Diagnosis"),
gr.Textbox(label="Confidence")
],
title="Breast Cancer Prediction App",
description="Enter the 30 medical features to predict whether the tumor is Benign or Malignant."
)
# ==============================
# Launch App
# ==============================
if __name__ == "__main__":
interface.launch() |