Create app.py

app.py

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+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 General Predictor")
+CORS(app)
+
+# Load trained model & helpers
+pipeline = joblib.load('pipeline.pkl')
+target_encoder = joblib.load('target_encoder.pkl')
+choice_code_map = pd.read_csv('choice_code_map.csv',index_col='Choice Code')
+
+# 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()
+
+        # 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
+        sample_df = pd.DataFrame([{
+            'Category': data['Category'],
+            'Rank': data['Rank'],
+            'Percentage': data['Percentage']
+            'Course Name' : data['Course Name']
+        }])
+
+        # 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']
+            course_name = row['Course Name']
+            results.append({
+                "rank": rank,
+                "choice_code": 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:
+        return jsonify({"error": str(e)}), 500
+
+# Run server
+if __name__ == '__main__':
+    app.run(debug=False, host='0.0.0.0', port=7860)