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@@ -1,16 +1,14 @@
-# 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 . .
+# Copy all files from deployment_files into the container
+COPY deployment_files/ ./
 
-# Install Python dependencies listed in requirements.txt
-RUN pip3 install -r requirements.txt
+# Install dependencies
+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
+# Run the Flask app with Gunicorn
+# Here: app.py is in deployment_files, Flask instance = store_sales_api
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:store_sales_api"]

app.py

@@ -1,70 +1,87 @@
-import streamlit as st
-import pandas as pd
-import requests
-
-# Set the title of the Streamlit app
-st.title("SuperKart Store Sales Prediction")
-
-# Section for online prediction
-st.subheader("Online Prediction")
-
-# Collect user input for store/product features
-product_weight = st.number_input("Product Weight", min_value=0.0, value=1.0)
-product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, value=10.0)
-product_mrp = st.number_input("Product MRP", min_value=0.0, value=50.0)
-store_establishment_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2015)
-product_sugar_content = st.selectbox("Product Sugar Content", ["Low", "Medium", "High"])
-product_type = st.selectbox("Product Type", ["Dairy", "Beverages", "Snacks", "Others"])
-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", ["Type 1", "Type 2", "Type 3", "Type 4"])
-store_id = st.text_input("Store Id", "S001")
-
-# Feature engineering for Store_Age
-store_age = 2025 - store_establishment_year
-
-# Convert user input into a DataFrame
-input_data = pd.DataFrame([{
-    'Product_Weight': product_weight,
-    'Product_Allocated_Area': product_allocated_area,
-    'Product_MRP': product_mrp,
-    'Store_Age': store_age,
-    'Product_Sugar_Content': product_sugar_content,
-    'Product_Type': product_type,
-    'Store_Size': store_size,
-    'Store_Location_City_Type': store_location_city_type,
-    'Store_Type': store_type,
-    'Store_Id': store_id
-}])
-
-# Make prediction when the "Predict" button is clicked
-if st.button("Predict"):
-    response = requests.post(
-        "https://Disha252001-SuperKart-Frontend.hf.space/v1/sale",
-        json=input_data.to_dict(orient='records')[0]
-    )
-    if response.status_code == 200:
-        prediction = response.json()['Predicted Store Sales']
-        st.success(f"Predicted Store Sales: {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 the Flask application
+store_sales_api = Flask("SuperKart Store Sales Predictor")
+
+# Load the trained machine learning model
+model = joblib.load("store_sales_prediction_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@store_sales_api.get('/')
+def home():
+    """
+    Root endpoint to check if the API is running.
+    """
+    return "Welcome to the SuperKart Store Sales Prediction API!"
+
+# Define an endpoint for single store sales prediction (POST request)
+@store_sales_api.post('/v1/sale')
+def predict_store_sales():
+    """
+    Handles POST requests for predicting sales of a single store.
+    Expects JSON input with store details.
+    """
+    # Get JSON input
+    store_data = request.get_json()
+
+    # Extract relevant features (update according to your dataset)
+    sample = {
+        'Store_Type': store_data['Store_Type'],
+        'Location_Type': store_data['Location_Type'],
+        'Region_Code': store_data['Region_Code'],
+        'Holiday': store_data['Holiday'],
+        'Discount': store_data['Discount']
+        # Add any other features you used during training
+    }
+
+    # Convert into DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (direct Store_Sales prediction)
+    predicted_sales = model.predict(input_data)[0]
+
+    # Convert to Python float and round
+    predicted_sales = round(float(predicted_sales), 2)
+
+    # Return JSON response
+    return jsonify({'Predicted Store Sales': predicted_sales})
+
+# Define an endpoint for batch prediction (POST request)
+@store_sales_api.post('/v1/salebatch')
+def predict_store_sales_batch():
+    """
+    Handles batch predictions for multiple stores.
+    Expects a CSV file with store details.
+    """
+    # Get uploaded CSV file
+    file = request.files['file']
+
+    # Read CSV into DataFrame
+    input_data = pd.read_csv(file)
+
+    # Make predictions
+    predicted_sales = model.predict(input_data).tolist()
+
+    # Convert to float and round
+    predicted_sales = [round(float(sale), 2) for sale in predicted_sales]
+
+    # Use Store IDs if available
+    if 'Store_ID' in input_data.columns:
+        store_ids = input_data['Store_ID'].tolist()
     else:
-        st.error("Error making prediction.")
-
-# 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 uploaded_file is not None:
-    if st.button("Predict Batch"):
-        response = requests.post(
-            "https://Disha252001-SuperKart-Frontend.hf.space/v1/salebatch",
-            files={"file": uploaded_file}
-        )
-        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.")
+        store_ids = list(range(1, len(predicted_sales)+1))  # Fallback index
+
+    # Create dictionary of predictions
+    output_dict = dict(zip(store_ids, predicted_sales))
+
+    # Return JSON
+    return jsonify(output_dict)
+
+# Run Flask app
+if __name__ == '__main__':
+    store_sales_api.run(debug=True)
+

requirements.txt

@@ -1,3 +1,11 @@
 pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
 requests==2.28.1
+uvicorn[standard]
 streamlit==1.43.2