import joblib import pandas as pd import numpy as np from flask import Flask, request, jsonify from flask_cors import CORS # Initialize Flask app app = Flask("Pharmacy College Predictor") CORS(app) # Load trained model & helpers pipeline = joblib.load('xgb_pipeline_gpu.pkl') target_encoder = joblib.load('target_encoder.pkl') choice_code_map = pd.read_csv('choice_code_map.csv').set_index('Choice Code') # Home route @app.get('/') def home(): return "✅ Welcome to Pharmacy College Predictor API!" # Predict route @app.post('/predict') def predict(): try: # Parse input JSON data = request.get_json() # Validate input required_fields = ['Category', 'Rank', 'Percentage'] missing = [f for f in required_fields if f not in data] if missing: return jsonify({"error": f"Missing fields: {missing}"}), 400 # Build DataFrame sample_df = pd.DataFrame([{ 'Category': data['Category'], 'Rank': data['Rank'], 'Percentage': data['Percentage'] }]) # Predict probabilities proba = pipeline.predict_proba(sample_df)[0] # Get top-20 indices (highest probabilities) top_20_idx = np.argsort(proba)[::-1][:20] # Normalize top-20 probs to sum to 100 top_20_probs = proba[top_20_idx] top_20_probs_normalized = top_20_probs / top_20_probs.sum() * 100 results = [] for rank, (idx, prob) in enumerate(zip(top_20_idx, top_20_probs_normalized), start=1): choice_code = target_encoder.inverse_transform([idx])[0] row = choice_code_map.loc[int(choice_code)] college_name = row['College Name'] results.append({ "rank": rank, "choice_code": choice_code, "college_name": college_name, "probability_percent": round(float(prob), 2) }) return jsonify({"top_20_predictions": results}) except Exception as e: return jsonify({"error": str(e)}), 500 # Run server if __name__ == '__main__': app.run(debug=False, host='0.0.0.0', port=7860)