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Dockerfile

@@ -1,14 +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"]
+# 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:sales_api"]

app.py

@@ -1,72 +1,83 @@
-import streamlit as st
+import joblib
 import pandas as pd
-import requests
-
-# ==================================
-# Streamlit UI
-# ==================================
-st.title("Product Store Sales Prediction App")
-st.write("This app predicts total sales for a product in a store based on product and store features.")
-st.write("Adjust the sliders and input fields below to get a prediction for a single product-store combination.")
-
-# -------------------------
-# Single record inputs
-# -------------------------
-Product_Id = st.number_input("Product ID", min_value=1, step=1)
-Product_Weight = st.number_input("Product Weight (kg)", min_value=0.0, step=0.01, value=0.5)
-Product_Sugar_Content = st.number_input("Sugar Content (%)", min_value=0.0, max_value=100.0, step=0.1, value=10.0)
-Product_Allocated_Area = st.number_input("Allocated Area (sq.m)", min_value=0.0, step=0.1, value=50.0)
-Product_Type = st.text_input("Product Type", value="Food")
-Product_MRP = st.number_input("Maximum Retail Price (MRP)", min_value=0.0, step=0.1, value=100.0)
-
-Store_Id = st.number_input("Store ID", min_value=1, step=1)
-Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, step=1, value=2000)
-Store_Size = st.number_input("Store Size (sq.m)", min_value=0.0, step=0.1, value=100.0)
-Store_Location_City_Type = st.selectbox("City Type", ["A", "B", "C"])
-Store_Type = st.text_input("Store Type", value="Supermarket")
-
-# -------------------------
-# Single prediction
-# -------------------------
-if st.button("Predict Sales", type='primary'):
-    input_data = {
-        'Product_Id': Product_Id,
-        'Product_Weight': Product_Weight,
-        'Product_Sugar_Content': Product_Sugar_Content,
-        'Product_Allocated_Area': Product_Allocated_Area,
-        'Product_Type': Product_Type,
-        'Product_MRP': Product_MRP,
-        'Store_Id': Store_Id,
-        'Store_Establishment_Year': Store_Establishment_Year,
-        'Store_Size': Store_Size,
-        'Store_Location_City_Type': Store_Location_City_Type,
-        'Store_Type': Store_Type
-    }
-
-    # Replace with your Hugging Face Space endpoint
-    API_URL = "https://abhishek1504-learning.hf.space/v1/sales"
-
-    response = requests.post(API_URL, json=input_data)
-    if response.status_code == 200:
-        result = response.json()
-        predicted_sales = result["Predicted_Product_Store_Sales_Total"]
-        st.success(f"💰 Predicted Total Product-Store Sales: **{predicted_sales:.2f} units**")
-    else:
-        st.error("Error in API request")
-
-# -------------------------
-# Batch prediction
-# -------------------------
-st.subheader("Batch Prediction")
-file = st.file_uploader("Upload CSV file with multiple product-store records", type=["csv"])
-
-if file is not None:
-    if st.button("Predict for Batch", type='primary'):
-        API_BATCH_URL = "https://abhishek1504-learning.hf.space/v1/salesbatch"
-        response = requests.post(API_BATCH_URL, files={"file": file})
-        if response.status_code == 200:
-            result = response.json()
-            st.header("Batch Prediction Results")
-            st.write(result)
-        else:
-            st.error("Error in API request")
+from flask import Flask, request, jsonify
+
+# Initialize Flask app
+sales_api = Flask("Product Store Sales Predictor")
+
+# Load the trained sales prediction model
+model = joblib.load("sales_prediction_model_v1_0.joblib")
+
+
+# Define a route for the home page
+@sales_api.get('/')
+def home():
+    return "Welcome to the Product Store Sales Prediction API!"
+
+# ==============================
+# Single record prediction endpoint
+# ==============================
+@sales_api.post('/v1/sales')
+def predict_sales():
+    try:
+        # Get JSON data from the request
+        data = request.get_json()
+
+        # Extract all required product/store features from the input data
+        sample = {
+            'Product_Id': data['Product_Id'],
+            'Product_Weight': data['Product_Weight'],
+            'Product_Sugar_Content': data['Product_Sugar_Content'],
+            'Product_Allocated_Area': data['Product_Allocated_Area'],
+            'Product_Type': data['Product_Type'],
+            'Product_MRP': data['Product_MRP'],
+            'Store_Id': data['Store_Id'],
+            'Store_Establishment_Year': data['Store_Establishment_Year'],
+            'Store_Size': data['Store_Size'],
+            'Store_Location_City_Type': data['Store_Location_City_Type'],
+            'Store_Type': data['Store_Type']
+        }
+
+        # Convert dictionary to DataFrame
+        input_df = pd.DataFrame([sample])
+
+        # Make prediction using the trained model
+        prediction = model.predict(input_df).tolist()[0]
+
+        # Return JSON response
+        return jsonify({'Predicted_Product_Store_Sales_Total': prediction})
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+# ==============================
+# Batch prediction endpoint
+# ==============================
+@sales_api.post('/v1/salesbatch')
+def predict_sales_batch():
+    try:
+        # Get uploaded CSV file from the request
+        file = request.files['file']
+
+        # Read CSV file into DataFrame
+        input_data = pd.read_csv(file)
+
+        # Make predictions on the batch
+        predictions = model.predict(input_data).tolist()
+
+        # Add predictions as new column
+        input_data['Predicted_Product_Store_Sales_Total'] = predictions
+
+        # Convert DataFrame to list of dicts for JSON output
+        result = input_data.to_dict(orient='records')
+
+        return jsonify(result)
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+# ==============================
+# Run Flask app
+# ==============================
+if __name__ == '__main__':
+    sales_api.run(debug=True)

requirements.txt

@@ -1,3 +1,10 @@
 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
-streamlit==1.43.2
+uvicorn[standard]