File size: 2,660 Bytes
a244ac5 f5d7c5a a244ac5 |
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 |
from flask import Flask, request, jsonify, render_template
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from src.pipelines.predict_pipeline import PredictPipeline, CustomData
application = Flask(__name__, template_folder='templates')
app = application
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict', methods=['POST', 'GET'])
def predict():
if request.method == 'GET':
return render_template('home.html')
try:
input_data = request.get_json()
if not input_data:
return jsonify({'error': 'No JSON data provided'}), 400
# Extract features
gender = input_data.get('gender')
race = input_data.get('race')
parental_education = input_data.get('parental_education')
lunch = input_data.get('lunch')
test_preparation_course = input_data.get('test_preparation_course')
reading_score = input_data.get('reading_score')
writing_score = input_data.get('writing_score')
# Validate all required fields
if not all([gender, race, parental_education, lunch, test_preparation_course,
reading_score is not None, writing_score is not None]):
return jsonify({'error': 'Missing required fields'}), 400
# Convert scores to float
try:
reading_score = float(reading_score)
writing_score = float(writing_score)
except ValueError:
return jsonify({'error': 'Scores must be numbers'}), 400
# Create CustomData instance
data = CustomData(
gender=gender,
race=race,
parental_level_of_education=parental_education,
lunch=lunch,
test_preparation_course=test_preparation_course,
reading_score=reading_score,
writing_score=writing_score
)
pred_df = data.get_data_as_data_frame()
predictor = PredictPipeline()
results = predictor.predict(pred_df)
return jsonify({
'predicted_math_score': float(results[0]),
'reading_score': reading_score,
'writing_score': writing_score
}), 200
except Exception as e:
print(f"Error: {str(e)}")
return jsonify({'error': f'Prediction failed: {str(e)}'}), 500
if __name__ == '__main__':
# This won't be used in production (Gunicorn runs it), but good for local testing
app.run(host='0.0.0.0', port=7860)
# https://huggingface.co/spaces/Aryanjaiswal78231/math-score-predictor |