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Dockerfile

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+FROM python:3.9-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy all files from the current directory to the container's working directory
+COPY . .
+
+# 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 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:super_kart_api"]

app.py

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+import joblib
+import numpy as np
+import pandas as pd
+import streamlit as st
+from flask import Flask, request, jsonify
+super_kart_api=Flask("Superkart_price_prediction")
+model=joblib.load('model.joblib')
+@super_kart_api.get('/')
+def home():
+  return "Welcome to SuperKart sales Prediction"
+@super_kart_api.post('/v1/spkart_single')
+def sale_pred_single():
+  sale_data=request.get_json()
+  # Read input data
+  sample={
+      'Product_Weight':sale_data['Product_Weight'],
+      'Product_Sugar_Content':sale_data['Product_Sugar_Content'],
+      'Product_Allocated_Area':sale_data['Product_Allocated_Area'],
+      'Product_Type':sale_data['Product_Type'],
+      'Product_MRP':sale_data['Product_MRP'],
+      'Store_Id':sale_data['Store_Id'],
+      'Store_Establishment_Year':sale_data['Store_Establishment_Year'],
+      'Store_Size':sale_data['Store_Size'],
+      'Store_Location_City_Type':sale_data['Store_Location_City_Type'],
+      'Store_Type':sale_data['Store_Type'],
+
+  }
+  input_data=pd.DataFrame([sample])
+  # Make predictions
+  predicted_sale=model.predict(input_data)[0]
+  # Create response
+  response={'Store_Outlet':sample['Store_Id'],"Sale":round(float(predicted_sale), 2)}
+  return jsonify(response)
+
+@super_kart_api.post('/v1/spkart_batch')
+def sale_pred_batch():
+    file = request.files['file']
+    print("File Received:", file.filename)
+    # Read input data
+    input_data = pd.read_csv(file)
+    # Make predictions
+    predicted_sale = model.predict(input_data).tolist()
+    # Add predictions to input data
+    input_data['Predicted_Sale'] = predicted_sale
+    # Group by Store_Id and sum the predicted sales
+    grouped_sales = input_data.groupby('Store_Id')['Predicted_Sale'].sum().to_dict()
+    # Create response
+    response = {
+        'store_sales': {store_id: round(float(sale), 2) for store_id, sale in grouped_sales.items()}
+    }
+    print("Final Response:", response)
+
+    return jsonify(response)
+
+
+
+
+if __name__=='__main__':
+  super_kart_api.run()

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c3d1c3d09fff9f3a1613ee7fb4c57dac39befa10e9cd38925e2c3525a954e726
+size 47878826

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.5.1
+Werkzeug==3.1.3
+flask==3.1.1
+gunicorn==23.0.0
+requests==2.32.3
+streamlit==1.43.2