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# Importing necessary modules from Flask to create the web application
from flask import Flask, request, render_template
# Importing additional necessary libraries
import numpy as np # For numerical operations
import pandas as pd # For data manipulation and creating DataFrame objects
# Importing custom modules: CustomData and PredictPipeline from the 'src.pipeline.predict_pipeline' module
from src.pipeline.predict_pipeline import CustomData, PredictPipeline
# Initializing the Flask application
app = Flask(__name__)
# Defining the route for the homepage of the web application
@app.route('/')
def index():
# Rendering the 'index.html' template when the root URL is accessed
return render_template('home.html')
# Defining the route for prediction, with both GET and POST methods allowed
@app.route('/predictdata', methods=['GET', 'POST'])
def predict_datapoint():
# If the request method is GET, render 'home.html'
if request.method == 'GET':
return render_template('home.html')
else:
try:
# Capture the form data (ensure form field names match these keys)
data = CustomData(
gender=request.form.get('gender'),
race_ethnicity=request.form.get('ethnicity'),
parental_level_of_education=request.form.get('parental_level_of_education'),
lunch=request.form.get('lunch'),
test_preparation_course=request.form.get('test_preparation_course'),
reading_score=float(request.form.get('reading_score')), # Ensuring correct casting
writing_score=float(request.form.get('writing_score')) # Ensuring correct casting
)
# Convert the collected form data into a pandas DataFrame
pred_df = data.get_data_as_data_frame()
print(f"Input DataFrame: \n{pred_df}")
# Initialize the prediction pipeline
predict_pipeline = PredictPipeline()
# Make the prediction
results = predict_pipeline.predict(pred_df)
print(f"Prediction Result: {results}")
# Render 'home.html' and display the prediction result
return render_template('home.html', results=results[0])
except Exception as e:
print(f"Error during prediction: {e}")
# If any error occurs, render the home page with an error message
return render_template('home.html', error="An error occurred during prediction. Please check your input.")
# Run the Flask app
if __name__ == "__main__":
# Running the app on host 0.0.0.0 (accessible from any device in the network), debug mode ON for development
app.run(host="0.0.0.0")
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