Update app.py

app.py

@@ -35,9 +35,6 @@ def predict():
         if missing:
             return jsonify({"error": f"Missing fields: {missing}"}), 400
 
-        # Optional field
-        desired_location = data.get("Location")  # None if not given
-
         # Build DataFrame
         df = pd.DataFrame([{
             'Category': data['Category'],
@@ -57,6 +54,7 @@ def predict():
         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
@@ -83,7 +81,6 @@ def predict():
                 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),
@@ -92,13 +89,6 @@ def predict():
                 "probability_percent": round(float(prob), 2)
             })
 
-        # 🔷 Filter by Location if provided
-        if desired_location:
-            results = [
-                r for r in results
-                if desired_location.lower() in r["college_name"].lower()
-            ]
-
         return jsonify({"top_20_predictions": results})
 
     except Exception as e:
@@ -109,4 +99,4 @@ def predict():
 
 # Run server
 if __name__ == '__main__':
-    app.run(debug=False, host='0.0.0.0', port=7860)
+    app.run(debug=False, host='0.0.0.0', port=7860)