Deployment Config

application.py

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+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, predictpipeline
+
+application = Flask(__name__)
+
+app = application
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/predictdata', methods = ['GET','POST'])
+def predict_datapoint():
+    if request.method == 'GET':
+        return render_template('home.html')
+    else:
+        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('writing_score')),
+            writing_score=float(request.form.get('reading_score'))
+        )
+
+        pred_df = data.get_data_as_data_frame()
+        print(pred_df)
+        print("Before prediction")
+
+        predict_pipeline = predictpipeline()
+        results = predict_pipeline.predict(pred_df)
+        print("after prediction")
+        return render_template('home.html', results = results[0])
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", debug = True)