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Dockerfile

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+FROM python:3.11-slim-buster                                                     # Uses a minimal Python 3.11 image as the base environment for deployment.
+
+WORKDIR /app                                                                     # Sets the working directory within the container to /app.
+
+COPY requirements.txt ./                                                         # Copies the requirements.txt file to the container's current working directory.
+
+RUN pip install --no-cache-dir --upgrade -r requirements.txt                     # Installs the required Python packages listed in requirements.txt without caching to reduce image size.               
+
+COPY app.py ./                                                                   # Copies the application script app.py into the container.                                   
+                                                      
+
+EXPOSE 5000                                                                      # Opens port 5000 so that the application can be accessed externally.
+
+CMD ["gunicorn", "--bind", "0.0.0.0:5000", "app:app"]                            # Starts the app using Gunicorn, binding it to port 5000 for web requests.

app.py

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+import os
+import joblib
+from flask import Flask, request, jsonify
+
+# Initialize flask app with a name
+app = Flask("SuperKart Sales Prediction App")
+
+# Load the trained model pipeline
+model_path = "/content/deployment_files/SuperKart_Sales_Prediction_Model.joblib"
+model = joblib.load(model_path)
+
+# Define a route for the home page
+@app.route("/")
+def home():
+    return "Welcome to the SuperKart Sales Prediction App!"
+
+# Define an endpoint for making predictions
+@app.route("/predict", methods=["POST"])
+def predict_sales():
+  if rf_pipeline is None:
+    return jsonify({"error": "Model not loaded"}), 500
+
+  try:
+        # Get JSON data from the request
+        data = request.get_json()
+        if not data:
+            return jsonify({"error": "No data provided"}), 400
+
+        # Extract features from the JSON data
+        input_df = pd.DataFrame([data])
+
+        return jsonify({"prediction": model.predict(input_df).tolist()})
+
+  except Exception as e:
+    return jsonify({"error": str(e)}), 500
+
+    if __name__ == "__main__":
+         app.run(host="0.0.0.0", port=5000,debug=True)

requirements.txt

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+numpy==2.0.2
+pandas==2.2.2
+scikit-learn==1.6.1
+matplotlib==3.10.0
+seaborn==0.13.2
+joblib==1.4.2
+xgboost==2.1.4
+requests==2.32.3
+huggingface_hub==0.30.1
+flask==3.0.0
+gunicorn==21.2.0
+

sales_prediction_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0f612465edf4838d6867ea8d66915d4f18bf9e845e7991124a54e3513dcece86
+size 1401539