Update app.py

app.py

@@ -1,20 +1,16 @@
-from flask import Flask
-from flask import request
-from flask import render_template
+import os
+from flask import Flask, request, render_template
 import numpy as np
 import pandas as pd
-
 from sklearn.preprocessing import StandardScaler
-from src.pipeline.predict_pipeline import CustomData
-from src.pipeline.predict_pipeline import PredictPipeline
+from src.pipeline.predict_pipeline import CustomData, PredictPipeline
 
 application = Flask(__name__)
 app = application
 
 @app.route('/predictdata', methods=['GET', 'POST'])
 def predict_datapoint():
     if request.method == 'POST':
-        # Extract form data
         gender = request.form.get('gender')
         race_ethnicity = request.form.get('ethnicity')
         parental_level_of_education = request.form.get('parental_level_of_education')
@@ -23,7 +19,6 @@ def predict_datapoint():
         reading_score = float(request.form.get('reading_score'))
         writing_score = float(request.form.get('writing_score'))
 
-        # Prepare data for prediction
         data = CustomData(
             gender=gender,
             race_ethnicity=race_ethnicity,
@@ -36,12 +31,10 @@ def predict_datapoint():
         pred_df = data.get_data_as_data_frame()
         print(pred_df)
 
-        print("Before Prediction")
         predict_pipeline = PredictPipeline()
         results = f"{predict_pipeline.predict(pred_df)[0]:.2f}"
         print("After Prediction")
 
-        # Pass form data and result to the template
         return render_template(
             'index.html',
             results=results,
@@ -54,8 +47,8 @@ def predict_datapoint():
             writing_score=writing_score
         )
     else:
-        # If GET request, render the template without pre-filled values
         return render_template('index.html')
 
 if __name__ == '__main__':
-    app.run(debug=True, host='0.0.0.0', port=5000)
+    port = int(os.environ.get('PORT', 5000))
+    app.run(debug=True, host='0.0.0.0', port=port)