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Dockerfile

@@ -1,16 +1,16 @@
-# Use a minimal base image with Python 3.9 installed
 FROM python:3.9-slim
 
-# Set the working directory inside the container to /app
+# Set the working directory inside the container
 WORKDIR /app
 
-# Copy all files from the current directory on the host to the container's /app directory
+# Copy all files from the current directory to the container's working directory
 COPY . .
 
-# Install Python dependencies listed in requirements.txt
-RUN pip3 install -r requirements.txt
+# Install dependencies from the requirements file without using cache to reduce image size
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
-# 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
+# Define the command to start the application using Gunicorn with 4 worker processes
+# - `-w 4`: Uses 4 worker processes for handling requests
+# - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
+# - `app:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:superkart_api"]

app.py

@@ -1,62 +1,82 @@
 
-import streamlit as st
-import requests
-
-st.title("Babatundes Superkart Sales Predictor") #title of the app.
-
-# Section for online prediction
-st.subheader("Input Product and Store Details for Sales Prediction")
-
-# Input fields for product and store data
-Product_Weight = st.number_input("Product Weight", min_value=0.0, value=15.0)
-Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
-Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, value=0.10)
-Product_MRP = st.number_input("Product Maximum Retail Price", min_value=0.10, value=35.00)
-Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
-Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
-Store_Type = st.selectbox("Store Type", ["Food Mart","Departmental Store","Supermarket Type1", "Supermarket Type2"])
-Product_Id_char = st.selectbox("Product Id Character", ["FD", "NC", "DR"])
-Store_Age_Years = st.number_input("Store Age - Years", min_value=0, value=15)
-Product_Type_Category = st.selectbox("Product Type Category", ["Perishables", "Non Perishables"])
-
-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_Cat": Product_Type_Category
-}
-
-if st.button("Predict", type='primary'):
-    response = requests.post("https://admattew-SuperkartPredictionBackEnd.hf.space/v1/predict", json=product_data)    #  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")
-
-# Section for batch prediction
-st.subheader("Batch Prediction")
-
-# Allow users to upload a CSV file for batch prediction
-uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
-
-# Make batch prediction when the "Predict Batch" button is clicked
-if st.button("Predict Batch"):
-    if uploaded_file:
-        response = requests.post("https://admattew/SuperkartPredictionBackEnd.hf.space/v1/predictbatch", files={"file": uploaded_file})  # Send file to Flask API
-        if response.status_code == 200:
-            predictions = response.json()
-            st.success("Batch predictions completed!")
-            st.write(predictions)  # Display the predictions
-        else:
-            st.error("Error making batch prediction.")
-else:
-    st.warning("Please upload a CSV file for batch prediction.")
+# Import necessary libraries
+import numpy as np
+import joblib  # For loading the serialized model
+import pandas as pd  # For data manipulation
+from flask import Flask, request, jsonify  # For creating the Flask API
 
+# Initialize Flask app with a name
+superkart_api = Flask("Babatundes Superkart Sales Predictor") #define the name of the app
+
+# Load the trained prediction model
+model = joblib.load("superkart_prediction_model.joblib") #define the location of the serialized model
+
+# Define a route for the home page
+@superkart_api.get('/')
+def home():
+    """
+    This function handles GET requests to the root URL ('/') of the API.
+    It returns a simple welcome message.
+    """
+    return "Welcome to Babatunde's Superkart Sales Predictor API!" #define a welcome message
+
+# Define an endpoint to predict churn for a single customer
+@superkart_api.post('/v1/predict')
+def predict_sales():
+    """
+    This function handles POST requests to the '/v1/predict' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted sales outcome price as a JSON response.
+    """
+    # Get JSON data from the request
+    data = request.get_json()
+
+    # Extract relevant product ans store features from the input data. The order of the column names matters.
+    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_Cat': data['Product_Type_Cat']
+    }
+
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make a sales prediction using the trained model
+    prediction = model.predict(input_data).tolist()[0]
+
+    # Return the prediction as a JSON response
+    return jsonify({'Sales': prediction})
+
+# Define an endpoint for batch prediction (POST request)
+@superkart_api.post('/v1/predictbatch')
+def predict_sales_batch():
+    """
+    This function handles POST requests to the '/v1/predictbatch' endpoint.
+    It expects a CSV file containing property details for multiple properties
+    and returns the predicted rental prices as a dictionary in the JSON response.
+    """
+    # Get the uploaded CSV file from the request
+    file = request.files['file']
+
+    # Read the CSV file into a Pandas DataFrame
+    input_data = pd.read_csv(file)
+
+    # Make predictions for all properties in the DataFrame
+    predicted_sales = model.predictbatch(input_data).tolist()
+
+    # Return the prediction as a JSON response
+    return jsonify({'Sales': predicted_sales})
+
+
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    superkart_api.run(debug=True)