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Dockerfile

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+FROM python:3.11-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(Flask instance `Superkart_api` inside `app.py`)
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:Superkart_api"]
+
+
+

Superkart_Sales_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c633b226e1ab2f7998b721fcab20514d2de2c88de42861f13589285db5832727
+size 689928

app.py

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+
+import numpy as np
+import pandas as pd
+import joblib
+from flask import Flask, request, jsonify
+from datetime import datetime
+
+# Initialize Flask app
+Superkart_api = Flask("SuperKart Sales Prediction")
+
+# Load the trained SuperKart sales model from the correct path
+model = joblib.load("backend_files/Superkart_Sales_prediction_model_v1_0.joblib")
+
+# Feature Engineering
+current_year = datetime.now().year
+perishable = {'Meat','Dairy','Fruits and Vegetables','Frozen Foods','Seafood','Breads','Breakfast'}
+
+def apply_feature_engineering(dataset):
+    
+    if 'Product_Id' in dataset.columns:
+        dataset['Product_Category'] = dataset['Product_Id'].str[:2].map({
+            'DR':'Drinks','NC':'Non-Consumable','FD':'Food & Veg'
+        }).fillna('Other')
+    if 'Store_Establishment_Year' in dataset.columns:
+        dataset['Store_Age'] = current_year - dataset['Store_Establishment_Year']
+    if 'Product_Type' in dataset.columns:
+        dataset['Food_Type'] = dataset['Product_Type'].apply(
+            lambda t: 'Perishable' if t in perishable else 'Non-Perishable'
+        )
+    return dataset
+
+# Home route
+@Superkart_api.get('/')
+def home():
+    return "Welcome to the SuperKart Sales Prediction!"
+
+# Single prediction endpoint
+@Superkart_api.post('/v1/sales')
+def predict_sales():
+    data = request.get_json()
+    sample = {
+        'Product_Id': data.get('Product_Id'),
+        'Product_Weight': data.get('Product_Weight'),
+        'Product_Sugar_Content': data.get('Product_Sugar_Content'),
+        'Product_Allocated_Area': data.get('Product_Allocated_Area'),
+        'Product_Type': data.get('Product_Type'),
+        'Product_MRP': data.get('Product_MRP'),
+        'Store_Id': data.get('Store_Id'),
+        'Store_Establishment_Year': data.get('Store_Establishment_Year'),
+        'Store_Size': data.get('Store_Size'),
+        'Store_Location_City_Type': data.get('Store_Location_City_Type'),
+        'Store_Type': data.get('Store_Type'),
+    }
+    input_df = pd.DataFrame([sample])
+    input_df = apply_feature_engineering(input_df)
+    predicted_sales = model.predict(input_df)[0]
+    return jsonify({'Predicted Sales': round(float(predicted_sales), 2)})
+
+# Batch prediction endpoint
+@Superkart_api.post('/v1/salesbatch')
+def predict_sales_batch():
+    file = request.files['file']
+    input_df = pd.read_csv(file)
+    input_df = apply_feature_engineering(input_df)
+    predicted_sales = model.predict(input_df).tolist()
+
+    if 'Product_Id' in input_df.columns:
+        product_ids = input_df['Product_Id'].tolist()
+        output_dict = dict(zip(product_ids, [round(float(x), 2) for x in predicted_sales]))
+    else:
+        output_dict = [round(float(x), 2) for x in predicted_sales]
+
+    return jsonify(output_dict)
+
+# Run the app
+if __name__ == '__main__':
+    Superkart_api.run(debug=True)

requirements.txt

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