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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:forecast_predictor_api"]

SuperKartbest_xgb.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:09cfbea7b6ef8456a4cb17a57b9bce6b8d1850b8a615194bfdd06adf3d2739c0
+size 1202904

app.py

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+# 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
+forecast_predictor_api = Flask("Forecast Predictor")
+
+# Load the trained machine learning model
+model = joblib.load("SuperKartbest_xgb.joblib")
+
+@forecast_predictor_api.get('/')
+def home():
+    return "Welcome to the Sales Prediction API!"
+
+@forecast_predictor_api.post('/v1/predict')
+def predict_single():
+    # Get the JSON data from the request body
+    product_data = request.get_json()
+    
+    # Extract relevant features from the JSON data
+    sample = {
+        'Product_Weight': product_data['Product_Weight'],
+        'Product_Allocated_Area': product_data['Product_Allocated_Area'],
+        'Product_MRP': product_data['Product_MRP'],
+        'Store_Establishment_Year': product_data['Store_Establishment_Year'],
+        'Product_Sugar_Content_No Sugar': product_data['Product_Sugar_Content_No Sugar'],
+        'Product_Sugar_Content_Regular': product_data['Product_Sugar_Content_Regular'],
+        'Product_Sugar_Content_reg': product_data['Product_Sugar_Content_reg'],
+        'Product_Type_Breads': product_data['Product_Type_Breads'],
+        'Product_Type_Breakfast': product_data['Product_Type_Breakfast'],
+        'Product_Type_Canned': product_data['Product_Type_Canned'],
+        'Product_Type_Dairy': product_data['Product_Type_Dairy'],
+        'Product_Type_Frozen Foods': product_data['Product_Type_Frozen Foods'],
+        'Product_Type_Fruits and Vegetables': product_data['Product_Type_Fruits and Vegetables'],
+        'Product_Type_Hard Drinks': product_data['Product_Type_Hard Drinks'],
+        'Product_Type_Health and Hygiene': product_data['Product_Type_Health and Hygiene'],
+        'Product_Type_Household': product_data['Product_Type_Household'],
+        'Product_Type_Meat': product_data['Product_Type_Meat'],
+        'Product_Type_Others': product_data['Product_Type_Others'],
+        'Product_Type_Seafood': product_data['Product_Type_Seafood'],
+        'Product_Type_Snack Foods': product_data['Product_Type_Snack Foods'],
+        'Product_Type_Soft Drinks': product_data['Product_Type_Soft Drinks'],
+        'Product_Type_Starchy Foods': product_data['Product_Type_Starchy Foods'],
+        'Store_Size_Medium': product_data['Store_Size_Medium'],
+        'Store_Size_Small': product_data['Store_Size_Small'],
+        'Store_Location_City_Type_Tier 2': product_data['Store_Location_City_Type_Tier 2'],
+        'Store_Location_City_Type_Tier 3': product_data['Store_Location_City_Type_Tier 3'],
+        'Store_Type_Food Mart': product_data['Store_Type_Food Mart'],
+        'Store_Type_Supermarket Type1': product_data['Store_Type_Supermarket Type1'],
+        'Store_Type_Supermarket Type2': product_data['Store_Type_Supermarket Type2']
+    }
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get log_price)
+    predicted_price = model.predict(input_data)[0]
+
+    # Convert predicted_price to Python float
+    predicted_price = round(float(predicted_price), 2)
+    # The conversion above is needed as we convert the model prediction (log price) to actual price using np.exp, which returns predictions as NumPy float32 values.
+    # When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error
+
+    # Return the actual price
+    return jsonify({'Predicted Price (in dollars)': predicted_price})
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == '__main__':
+    forecast_predictor_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