File size: 3,140 Bytes
683a8c7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
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) |