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Dockerfile

@@ -1,16 +1,12 @@
-# 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
+RUN pip install --no-cache-dir -r requirements.txt
+
+EXPOSE 7860
+
+CMD ["gunicorn", "-w", "2", "-b", "0.0.0.0:7860", "app:superkart_api"]
 
-# Define the command to run the Streamlit app on port 8501 and make it accessible externally
-CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
 
-# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -1,39 +1,41 @@
+import numpy as np
+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
 
-import streamlit as st
-import requests
-
-st.title("Product Store Sales Prediction App") #Complete the code to define the title of the app.
-
-# Input fields for product and store data
-Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.66)
-Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
-Product_Allocated_Area = st.selectbox("Product Allocated Area", ["Small", "Medium", "Large"]) #Complete the code to define the UI element for Product_Allocated_Area
-Product_MRP = st.number_input("Product MRP", min_value=0.0, value=100.0) #Complete the code to define the UI element for Product_MRP
-Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])   #Complete the code to define the UI element for Store_Size
-Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"]) #Complete the code to define the UI element for Store_Location_City_Type
-Store_Type = st.selectbox("Store Type", ["Type 1", "Type 2", "Type 3", "Type 4"]) #Complete the code to define the UI element for Store_Type
-Product_Id_char = st.text_input("Product ID (char)", value="FDX") #Complete the code to define the UI element for Product_Id_char
-Store_Age_Years = st.number_input("Store Age (Years)", min_value=0, value=5) #Complete the code to define the UI element for Store_Age_Years
-Product_Type_Category = st.selectbox("Product Type Category", ["Food", "Non-Food", "Drinks"]) #Complete the code to define the UI element for Product_Type_Category
-
-product_data = {
-    "Product_Weight": Product_Weight,
-    "Product_Sugar_Content": Product_Sugar_Content,
-    "Product_Allocated_Area": Product_Allocated_Area,
-    "Product_MRP": Product_MRP,
-    "Store_Size": Store_Size,
-    "Store_Location_City_Type": Store_Location_City_Type,
-    "Store_Type": Store_Type,
-    "Product_Id_char": Product_Id_char,
-    "Store_Age_Years": Store_Age_Years,
-    "Product_Type_Category": Product_Type_Category
-}
-
-if st.button("Predict", type='primary'):
-    response = requests.post("https://Hunagypsy-my-docker-space.hf.space/v1/predict", json=product_data)    # Complete the code to enter user name and space name to correctly define the endpoint
-    if response.status_code == 200:
-        result = response.json()
-        predicted_sales = result["Sales"]
-        st.write(f"Predicted Product Store Sales Total: ₹{predicted_sales:.2f}")
-    else:
-        st.error("Error in API request")
+# Create Flask app
+superkart_api = Flask(__name__)
+
+# Load the trained model
+model = joblib.load("tuned_gradient_boosting_regressor_model.joblib")
+
+@superkart_api.get('/')
+def home():
+    return "Welcome to the Superkart Sales Prediction API!"
+
+@superkart_api.post('/v1/predict')
+def predict_sales():
+    data = request.get_json()
+
+    sample = {
+        'Product_Weight': data['Product_Weight'],
+        'Product_Sugar_Content': data['Product_Sugar_Content'],
+        'Product_Allocated_Area': data['Product_Allocated_Area'],
+        'Product_MRP': data['Product_MRP'],
+        'Store_Size': data['Store_Size'],
+        'Store_Location_City_Type': data['Store_Location_City_Type'],
+        'Store_Type': data['Store_Type'],
+        'Product_Id_char': data['Product_Id_char'],
+        'Store_Age_Years': data['Store_Age_Years'],
+        'Product_Type_Category': data['Product_Type_Category']
+    }
+
+    input_df = pd.DataFrame([sample])
+    prediction = model.predict(input_df).tolist()[0]
+
+    return jsonify({'Sales': prediction})
+
+if __name__ == '__main__':
+    import os
+    port = int(os.environ.get("PORT", 7860))
+    superkart_api.run(host="0.0.0.0", port=port)

requirements.txt

@@ -1,2 +1,10 @@
+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
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
 requests==2.32.3
-streamlit==1.45.0