Update app.py

app.py

@@ -1,61 +1,61 @@
-# 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", debug=True)
+# 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", debug=True)