Upload app.py

app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import pandas as pd
+import numpy as np
+import joblib
+from io import StringIO
+import os
+
+# Load model and columns
+MODEL_PATH = "xgboost_credit_model.joblib"
+COLS_PATH = "train_features_columns.joblib"
+METRICS_PATH = "evaluation_metrics.joblib"
+
+model = joblib.load(MODEL_PATH)
+train_features_columns = joblib.load(COLS_PATH)
+if os.path.exists(METRICS_PATH):
+    evaluation_metrics = joblib.load(METRICS_PATH)
+else:
+    evaluation_metrics = {}
+
+app = Flask(__name__)
+CORS(app)
+
+def preprocess_user_data(user_df, train_columns):
+    # One-hot encode categorical columns
+    categorical_cols = user_df.select_dtypes(include=['object']).columns.tolist()
+    user_df = pd.get_dummies(user_df, columns=categorical_cols, drop_first=True)
+    # Add missing columns
+    missing_cols = set(train_columns) - set(user_df.columns)
+    for c in missing_cols:
+        user_df[c] = 0
+    # Remove extra columns
+    extra_cols = set(user_df.columns) - set(train_columns)
+    user_df = user_df.drop(columns=list(extra_cols), errors='ignore')
+    # Reorder
+    user_df = user_df[train_columns]
+    return user_df
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        user_input = request.json
+        user_df = pd.DataFrame([user_input])
+        user_features_processed = preprocess_user_data(user_df.copy(), train_features_columns)
+        prediction = model.predict(user_features_processed)
+        result = "Eligible" if prediction[0] == 1 else "Not Eligible"
+        return jsonify({
+            'prediction': result,
+            'metrics': evaluation_metrics
+        })
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/predict_csv', methods=['POST'])
+def predict_csv():
+    try:
+        if 'file' not in request.files:
+            return jsonify({'error': 'No file part in the request'}), 400
+        file = request.files['file']
+        if file.filename == '':
+            return jsonify({'error': 'No selected file'}), 400
+        csv_data = StringIO(file.read().decode('utf-8'))
+        input_df = pd.read_csv(csv_data)
+        # Remove Creditworthy if present
+        if 'Creditworthy' in input_df.columns:
+            input_df = input_df.drop(columns=['Creditworthy'])
+        input_df = input_df.dropna(axis=1, how='all')
+        user_features_processed = preprocess_user_data(input_df.copy(), train_features_columns)
+        predictions = model.predict(user_features_processed)
+        input_df['Creditworthy_Prediction'] = np.where(predictions == 1, 'Eligible', 'Not Eligible')
+        results = input_df.to_dict('records')
+        return jsonify({
+            'predictions': results,
+            'metrics': evaluation_metrics,
+            'fairness_metrics': {},
+            'fairness_observation': "Fairness metrics require ground truth labels and are not available for this upload."
+        })
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)