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("Engineering College Predictor")
CORS(app)

# 🔷 Load trained model & helpers
model = joblib.load('xgb_best_model.joblib')
label_encoder = joblib.load('label_encoder.joblib')
feature_columns = joblib.load('feature_columns.joblib')
choice_code_map = pd.read_csv('choice_code_map.csv', index_col='Choice Code')

print("✅ Model and helpers loaded.")

# Home route
@app.get('/')
def home():
    return "✅ Welcome to Engineering College Predictor API!"

# Predict route
@app.post('/predict')
def predict():
    try:
        # Parse input JSON
        data = request.get_json()
        print(f"📥 Received data: {data}")

        # Validate input
        required_fields = ['Category', 'Rank', 'Percentage', 'Course Name']
        missing = [f for f in required_fields if f not in data]
        if missing:
            return jsonify({"error": f"Missing fields: {missing}"}), 400

        # Build DataFrame
        df = pd.DataFrame([{
            'Category': data['Category'],
            'Rank': data['Rank'],
            'Percentage': data['Percentage'],
            'Course Name': data['Course Name']
        }])

        # Feature engineering
        df["Rank_log"] = np.log1p(df["Rank"])
        df["Percentage_bin"] = pd.cut(
            df["Percentage"], bins=[0,50,60,70,80,90,100], labels=False
        )

        # One-hot encode and align with training columns
        X_query = pd.get_dummies(df)
        X_query["Rank_log"] = df["Rank_log"]
        X_query["Percentage_bin"] = df["Percentage_bin"]

        # Ensure all training columns exist
        for col in feature_columns:
            if col not in X_query.columns:
                X_query[col] = 0

        X_query = X_query[feature_columns]

        # Predict probabilities
        proba = model.predict_proba(X_query)[0]

        # Get top-20 indices
        top_20_idx = np.argsort(proba)[::-1][:20]

        # Normalize top-20 probabilities
        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 = label_encoder.inverse_transform([idx])[0]
            if choice_code not in choice_code_map.index:
                college_name = "Unknown"
                course_name = "Unknown"
            else:
                row = choice_code_map.loc[int(choice_code)]
                college_name = row['College Name']
                course_name = row['Course Name']
            results.append({
                "rank": rank,
                "choice_code": int(choice_code),
                "college_name": college_name,
                "course_name": course_name,
                "probability_percent": round(float(prob), 2)
            })

        return jsonify({"top_20_predictions": results})

    except Exception as e:
        import traceback
        traceback.print_exc()
        return jsonify({"error": str(e)}), 500


# Run server
if __name__ == '__main__':
    app.run(debug=False, host='0.0.0.0', port=7860)