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| from flask import Flask, request, render_template | |
| import pandas as pd | |
| import joblib | |
| app = Flask(__name__) | |
| # Load your pre-trained model | |
| model = joblib.load('rf_model.pkl') | |
| threshold = 0.7032966204148201 | |
| def home(): | |
| return render_template('index.html') | |
| def predict(): | |
| try: | |
| # Extract values from the form | |
| features = [ | |
| float(request.form.get('AppendixDiameter')), | |
| float(request.form.get('ReboundTenderness')), | |
| float(request.form.get('CoughingPain')), | |
| float(request.form.get('FreeFluids')), | |
| float(request.form.get('MigratoryPain')), | |
| float(request.form.get('BodyTemp')), | |
| float(request.form.get('KetonesInUrine')), | |
| float(request.form.get('Nausea')), | |
| float(request.form.get('WBCCount')), | |
| float(request.form.get('NeutrophilPerc')), | |
| float(request.form.get('CRPEntry')), | |
| float(request.form.get('Peritonitis')) | |
| ] | |
| print("features",features) | |
| except Exception as e: | |
| return render_template('index.html', prediction_text="Error in input: " + str(e)) | |
| # Create a DataFrame with proper column names | |
| columns = ['AppendixDiameter', 'ReboundTenderness', 'CoughingPain', 'FreeFluids', 'MigratoryPain', | |
| 'BodyTemp', 'KetonesInUrine', 'Nausea', 'WBCCount', 'NeutrophilPerc', 'CRPEntry', 'Peritonitis'] | |
| data = pd.DataFrame([features], columns=columns) | |
| # Get the predicted probability and apply your threshold | |
| pred_prob = model.predict_proba(data)[0][1] | |
| prediction = 1 if pred_prob >= threshold else 0 | |
| result = "Appendicitis" if prediction == 1 else "No Appendicitis" | |
| return render_template('index.html', prediction_text=f"Prediction: {result}") | |
| if __name__ == "__main__": | |
| app.run(debug=True) | |