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Extraa-Learn-backend2

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import numpy as np
import pandas as pd
import joblib
from flask import Flask, request, jsonify
from flask_cors import CORS

# Initialize Flask app
Extraa_Learn_api = Flask("Extraa_Learn_api")
CORS(Extraa_Learn_api)

# Load the trained model pipeline (preprocessing + model)
model = joblib.load("/content/drive/My Drive/Colab Notebooks/deployment_files/Extraa_Learn_model_v1_0.joblib")

# Health check route
@Extraa_Learn_api.get('/')
def home():
    return "✅ Welcome to the Extraa-Learn_API"

# Prediction route
@Extraa_Learn_api.post('/v1/status')
def predict_status():
    try:
        # Parse JSON payload
        data = request.get_json()
        print("Raw incoming data:", data)

        # Validate expected fields
        required_fields = [
            'age',
            'current_occupation',
            'first_interaction',
            'profile_completed',
            'website_visits',
            'time_spent_on_website',
            'page_views_per_visit',
            'last_activity',
            'print_media_type1',
            'print_media_type2',
            'digital_media',
            'educational_channels',
            'referral'
        ]
        missing_fields = [f for f in required_fields if f not in data]
        if missing_fields:
            return jsonify({'error': f"Missing fields: {missing_fields}"}), 400

        # Convert and transform input
        sample = {
            'age': float(data['age']),
            'current_occupation': data['current_occupation'],
            'first_interaction': data['first_interaction'],
            'profile_completed': data['profile_completed'],
            'website_visits': data['website_visits'],
            'time_spent_on_website': ['time_spent_on_website']),
            'page_views_per_visit': data['page_views_per_visit'],
            'last_activity': data['last_activity'],
            'print_media_type1': data['print_media_type1'],
            'print_media_type2': data['print_media_type2']),
            'digital_media': data['digital_media'],
            'educational_channels': data['educational_channels'],
            'referral': data['referral']
        }

        input_df = pd.DataFrame([sample])
        print("Transformed input for model:
", input_df)

        # Make prediction
        prediction = model.predict(input_df).tolist()[0]
        return jsonify({'Status': prediction})

    except Exception as e:
        print("❌ Error during prediction:", str(e))
        return jsonify({'error': f"Prediction failed: {str(e)}"}), 500

# Run the app (for local testing only)
if __name__ == '__main__':
    Extraa_Learn_api.run(debug=True)


	import numpy as np
	import pandas as pd
	import joblib
	from flask import Flask, request, jsonify
	from flask_cors import CORS

	# Initialize Flask app
	Extraa_Learn_api = Flask("Extraa_Learn_api")
	CORS(Extraa_Learn_api)

	# Load the trained model pipeline (preprocessing + model)
	model = joblib.load("/content/drive/My Drive/Colab Notebooks/deployment_files/Extraa_Learn_model_v1_0.joblib")

	# Health check route
	@Extraa_Learn_api.get('/')
	def home():
	return "✅ Welcome to the Extraa-Learn_API"

	# Prediction route
	@Extraa_Learn_api.post('/v1/status')
	def predict_status():
	try:
	# Parse JSON payload
	data = request.get_json()
	print("Raw incoming data:", data)

	# Validate expected fields
	required_fields = [
	'age',
	'current_occupation',
	'first_interaction',
	'profile_completed',
	'website_visits',
	'time_spent_on_website',
	'page_views_per_visit',
	'last_activity',
	'print_media_type1',
	'print_media_type2',
	'digital_media',
	'educational_channels',
	'referral'
	]
	missing_fields = [f for f in required_fields if f not in data]
	if missing_fields:
	return jsonify({'error': f"Missing fields: {missing_fields}"}), 400

	# Convert and transform input
	sample = {
	'age': float(data['age']),
	'current_occupation': data['current_occupation'],
	'first_interaction': data['first_interaction'],
	'profile_completed': data['profile_completed'],
	'website_visits': data['website_visits'],
	'time_spent_on_website': ['time_spent_on_website']),
	'page_views_per_visit': data['page_views_per_visit'],
	'last_activity': data['last_activity'],
	'print_media_type1': data['print_media_type1'],
	'print_media_type2': data['print_media_type2']),
	'digital_media': data['digital_media'],
	'educational_channels': data['educational_channels'],
	'referral': data['referral']
	}

	input_df = pd.DataFrame([sample])
	print("Transformed input for model:
	", input_df)

	# Make prediction
	prediction = model.predict(input_df).tolist()[0]
	return jsonify({'Status': prediction})

	except Exception as e:
	print("❌ Error during prediction:", str(e))
	return jsonify({'error': f"Prediction failed: {str(e)}"}), 500

	# Run the app (for local testing only)
	if __name__ == '__main__':
	Extraa_Learn_api.run(debug=True)