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backend_files/app.py

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+
+import numpy as np
+import pandas as pd
+import joblib
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+
+# Initialize Flask app
+Extraa_Learn_api = Flask("Extraa_Learn_api")
+CORS(Extraa_Learn_api)
+
+# Load the trained model pipeline (preprocessing + model)
+model = joblib.load("/content/drive/My Drive/Colab Notebooks/deployment_files/Extraa_Learn_model_v1_0.joblib")
+
+# Health check route
+@Extraa_Learn_api.get('/')
+def home():
+    return "✅ Welcome to the Extraa_Learn_API"
+
+# Prediction route
+@Extraa_Learn_api.post('/v1/predict')
+def predict_sales():
+    try:
+        # Parse JSON payload
+        data = request.get_json()
+        print("Raw incoming data:", data)
+
+        # Validate expected fields
+        required_fields = [
+            'age',
+            'current_occupation',
+            'first_interaction',
+            'profile_completed',
+            'website_visits',
+            'time_spent_on_website',
+            'page_views_per_visit',
+            'last_activity',
+            'print_media_type1',
+            'print_media_type2',
+            'digital_media',
+            'educational_channels',
+            'referral'
+        ]
+        missing_fields = [f for f in required_fields if f not in data]
+        if missing_fields:
+            return jsonify({'error': f"Missing fields: {missing_fields}"}), 400
+
+        # Convert and transform input
+        sample = {
+            'age': float(data['age']),
+            'current_occupation': data['current_occupation'],
+            'first_interaction': data['first_interaction'],
+            'profile_completed': data['profile_completed'],
+            'website_visits': np.log1p(float(data['website_visits'])),  # transform here
+            'time_spent_on_website': float(data['time_spent_on_website']),
+            'page_views_per_visit': data['page_views_per_visit'],
+            'last_activity': data['last_activity'],
+            'print_media_type1': data['print_media_type1'],
+            'print_media_type2': int(data['print_media_type2']),
+            'digital_media': data['digital_media'],
+            'educational_channels': data['educational_channels'],
+            'referral': data['referral']
+        }
+
+        input_df = pd.DataFrame([sample])
+        print("Transformed input for model:
+", input_df)
+
+        # Make prediction
+        prediction = model.predict(input_df).tolist()[0]
+        return jsonify({'Status': prediction})
+
+    except Exception as e:
+        print("❌ Error during prediction:", str(e))
+        return jsonify({'error': f"Prediction failed: {str(e)}"}), 500
+
+# Run the app (for local testing only)
+if __name__ == '__main__':
+    Extraa_Learn_api.run(debug=True)

backend_files/requirements.txt

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+# Core libraries
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+seaborn==0.13.2
+joblib==1.4.2
+xgboost==2.1.4
+
+# Flask web server
+flask==2.2.2
+flask-cors==3.0.10
+gunicorn==20.1.0
+Werkzeug==2.2.2
+
+# For API testing
+requests==2.32.3

frontend_files/Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9-slim
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Define the command to run the Streamlit app on port 7860 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

frontend_files/app.py

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+
+# Streamlit Web App for Extraa_Learn prediction
+import streamlit as st
+import requests
+import numpy as np
+
+# App Title
+st.title("Extraa_Learn_Prediction")
+
+# Instructions
+#st.markdown("🔍 Enter product and store attributes to forecast **monthly product sales revenue**.\n\n_All sales are reported in ($) USD._")
+
+# User Inputs
+Age = st.number_input("age)", min_value=0.0, value=100)
+current_occupation = st.selectbox("current_occupation", ["Low Sugar", "Regular", "No Sugar"])
+first_interaction = st.number_input("first_interaction (linear in.)", min_value=0.0, value=100.0)
+profile_completed = st.number_input("profile_completed)", min_value=0.0, value=150.0)
+website_visits = st.selectbox("website_visits", ["Small", "Medium", "High"])
+time_spent_on_website = st.selectbox("time_spent_on_website", ["Tier 1", "Tier 2", "Tier 3"])
+page_views_per_visit = st.selectbox("page_views_per_visit", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
+#Store_Age_Years = st.slider("Store Age (years)", min_value=0, max_value=30, value=10)#
+last_activity = st.number_input("last_activity)", min_value=0, value=10)
+print_media_type1 = st.selectbox("print_media_type1", ["Perishables", "Non Perishables"])
+print_media_type2 = st.selectbox("print_media_type2", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
+#Store_Age_Years = st.slider("Store Age (years)", min_value=0, max_value=30, value=10)#
+digital_media = st.number_input("digital_media)", min_value=0, value=10)
+educational_channels = st.selectbox("educational_channels", ["Perishables", "Non Perishables"])
+referral= st.selectbox("referral", ["Perishables", "Non Perishables"])
+
+# Apply log1p transform (must match backend model training)
+Product_Allocated_Area_Log = np.log1p(Product_Allocated_Area)
+
+# Prepare JSON payload for the backend
+product_data = {
+    "Age": str(Product_Weight),
+    "current_occupation ": Product_Sugar_Content,
+    
+# Trigger Prediction
+if st.button("Predict", type='primary'):
+    try:
+        response = requests.post(
+            "https://HSSHETTY01-Extraa_Learns-backend2.hf.space/v1/predict",
+            json=product_data
+        )
+        if response.status_code == 200:
+            result = response.json()
+            predicted_sales = result["Status"]
+            st.success(f"📈 Ststus: **${status:,.2f} USD**")
+        else:
+            st.error("❌ API Error: Please verify input values or try again later.")
+    except Exception as e:
+        st.error(f"⚠️ Connection error: {e}")

frontend_files/requirements.txt

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+requests==2.32.3
+streamlit==1.45.0