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@@ -2,5 +2,4 @@ FROM python:3.9-slim
 WORKDIR /app
 COPY . .
 RUN pip install --no-cache-dir -r requirements.txt
-# Important: app:app refers to app.py and the Flask instance named 'app'
 CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:app"]

app.py

@@ -3,13 +3,9 @@ import joblib
 import pandas as pd
 from flask import Flask, request, jsonify
 
-# Create the Flask app
 app = Flask(__name__)
-
-# Load the model
 model = joblib.load('Extraalearn.joblib')
 
-# Define the expected feature columns from training (excluding 'status')
 EXPECTED_FEATURES = [
     'age', 'website_visits', 'time_spent_on_website', 'page_views_per_visit',
     'current_occupation_Student', 'current_occupation_Unemployed',
@@ -25,46 +21,27 @@ def home():
     return 'ExtraaLearn API is Running'
 
 @app.post('/v1/predict')
-def predict_conversion_probability(): # Renamed endpoint for clarity
+def predict():
     try:
         data = request.get_json()
-
-        # Create a dictionary to hold the processed features
-        processed_data = {}
-
-        # 1. Map Numerical Features
-        processed_data['age'] = float(data.get('age', 0))
-        processed_data['website_visits'] = float(data.get('website_visits', 0))
-        processed_data['time_spent_on_website'] = float(data.get('time_spent_on_website', 0))
-        processed_data['page_views_per_visit'] = float(data.get('page_views_per_visit', 0))
-
-        # 2. Map Categorical Features (One-Hot Encoding logic)
-        # Note: 'Professional', 'Mobile App', 'High', 'Email Activity', and 'No' are the baselines (drop_first)
-
-        occ = data.get('current_occupation', '')
-        processed_data['current_occupation_Student'] = 1 if occ == 'Student' else 0
-        processed_data['current_occupation_Unemployed'] = 1 if occ == 'Unemployed' else 0
-
-        processed_data['first_interaction_Website'] = 1 if data.get('first_interaction', '') == 'Website' else 0
-
-        prof = data.get('profile_completed', '')
-        processed_data['profile_completed_Low'] = 1 if prof == 'Low' else 0
-        processed_data['profile_completed_Medium'] = 1 if prof == 'Medium' else 0
-
-        last = data.get('last_activity', '')
-        processed_data['last_activity_Phone Activity'] = 1 if last == 'Phone Activity' else 0
-        processed_data['last_activity_Website Activity'] = 1 if last == 'Website Activity' else 0
-
-        processed_data['print_media_type1_Yes'] = 1 if data.get('print_media_type1', '') == 'Yes' else 0
-        processed_data['print_media_type2_Yes'] = 1 if data.get('print_media_type2', '') == 'Yes' else 0
-        processed_data['digital_media_Yes'] = 1 if data.get('digital_media', '') == 'Yes' else 0
-        processed_data['educational_channels_Yes'] = 1 if data.get('educational_channels', '') == 'Yes' else 0
-        processed_data['referral_Yes'] = 1 if data.get('referral', '') == 'Yes' else 0
-
-        # Convert dictionary to DataFrame with the exact training order
-        df_final = pd.DataFrame([processed_data])[EXPECTED_FEATURES]
-
-        # Predict probability of conversion (positive class is 1)
+        p = {}
+        p['age'] = float(data.get('age', 0))
+        p['website_visits'] = float(data.get('website_visits', 0))
+        p['time_spent_on_website'] = float(data.get('time_spent_on_website', 0))
+        p['page_views_per_visit'] = float(data.get('page_views_per_visit', 0))
+        p['current_occupation_Student'] = 1 if data.get('current_occupation') == 'Student' else 0
+        p['current_occupation_Unemployed'] = 1 if data.get('current_occupation') == 'Unemployed' else 0
+        p['first_interaction_Website'] = 1 if data.get('first_interaction') == 'Website' else 0
+        p['profile_completed_Low'] = 1 if data.get('profile_completed') == 'Low' else 0
+        p['profile_completed_Medium'] = 1 if data.get('profile_completed') == 'Medium' else 0
+        p['last_activity_Phone Activity'] = 1 if data.get('last_activity') == 'Phone Activity' else 0
+        p['last_activity_Website Activity'] = 1 if data.get('last_activity') == 'Website Activity' else 0
+        p['print_media_type1_Yes'] = 1 if data.get('print_media_type1') == 'Yes' else 0
+        p['print_media_type2_Yes'] = 1 if data.get('print_media_type2') == 'Yes' else 0
+        p['digital_media_Yes'] = 1 if data.get('digital_media') == 'Yes' else 0
+        p['educational_channels_Yes'] = 1 if data.get('educational_channels') == 'Yes' else 0
+        p['referral_Yes'] = 1 if data.get('referral') == 'Yes' else 0
+        df_final = pd.DataFrame([p])[EXPECTED_FEATURES]
         prediction_proba = model.predict_proba(df_final)[:, 1].tolist()[0] # Use predict_proba
         return jsonify({'Conversion_Probability': float(prediction_proba)}) # Changed key to Conversion_Probability
     except Exception as e:

requirements.txt

@@ -1,3 +1,4 @@
+writefile backend_files/requirements.txt
 pandas==2.2.2
 numpy==2.0.2
 scikit-learn==1.6.1