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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:superkart_sales_predictor_api"]

SuperKart_sales_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:304efbec744043097c7349a7ea25cf13aa75b0592a670f9bcda49209efbf25d1
+size 18545843

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
+superkart_sales_predictor_api = Flask("SuperKart Sales Predictor")
+
+# Load the trained machine learning model
+model = joblib.load("SuperKart_sales_prediction_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@superkart_sales_predictor_api.post('/v1/sales')
+def predict_total_sales():
+    data = request.get_json()
+
+    # Check if batch or single
+    if isinstance(data, dict):
+        data = [data]  # convert single input to list for consistency
+
+    samples = []
+    for property_data in data:
+        sample = {
+            'product_weight': property_data['Product_Weight'],
+            'product_sugar_content': property_data['Product_Sugar_Content'],
+            'product_type': property_data['Product_Type'],
+            'store_size': property_data['Store_Size'],
+            'store_location_city_type': property_data['Store_Location_City_Type'],
+            'store_type': property_data['Store_Type'],
+            'store_establishment_year': property_data['Store_Establishment_Year'],
+            'product_allocated_area': property_data['Product_Allocated_Area'],
+            'product_mrp': property_data['Product_MRP']
+        }
+        samples.append(sample)
+
+    input_df = pd.DataFrame(samples)
+
+    # Predict log sales total
+    log_predictions = model.predict(input_df)
+
+    # Convert log-predicted values to actual sales
+    actual_predictions = [round(float(np.exp(pred)), 2) for pred in log_predictions]
+
+    return jsonify({'Predicted Product Store Sales Totals': actual_predictions})
+
+if __name__ == '__main__':
+  app.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