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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", "-b", "0.0.0.0:7860", "app:sales_prediction_api"]

app.py

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+import pandas as pd
+import numpy as np
+import joblib
+from flask import Flask,jsonify,request
+
+# Initialize flask app
+
+sales_prediction_api=Flask("Forecasted Product Sales Predictor")
+
+# load the model
+
+model=joblib.load('sales_prediction_model_v1_0.joblib')
+
+# create home endpoint
+
+@sales_prediction_api.get('/')
+def home():
+  return "Welcome to the Superkart product sales forecast API"
+
+# create health check endpoint
+
+#@sales_prediction_api.get('/health')
+#def health_check():
+#  return jsonify({"status": "ok"}), 200
+
+# create endpoint for single row data processing
+
+@sales_prediction_api.post('/v1/data')
+def predict_data():
+  data=request.get_json()
+
+  user_input={
+ 'Product_Weight':data['Product_Weight'],
+ 'Product_Sugar_Content':data['Product_Sugar_Content'],
+ 'Product_Allocated_Area':data['Product_Allocated_Area'],
+ 'Product_Type':data['Product_Type'],
+ 'Product_MRP':data['Product_MRP'],
+ 'Store_Id':data['Store_Id'],
+ 'Store_Establishment_Year':data['Store_Establishment_Year'],
+ 'Store_Size':data['Store_Size'],
+ 'Store_Location_City_Type':data['Store_Location_City_Type'],
+ 'Store_Type':data['Store_Type']
+  }
+
+  df=pd.DataFrame([user_input])
+
+  prediction=model.predict(df).tolist()[0]
+
+  return jsonify({'prediction':prediction})
+
+
+# create endpoint for batch processing
+
+@sales_prediction_api.post('/v1/databatch')
+def predict_data_batch():
+
+  file1=request.files['file']
+
+  df_input=pd.read_csv(file1)
+
+  predictionlist=model.predict(df_input.drop(['Product_Id'],axis=1)).tolist()
+  idlist=df_input.Product_Id.values.tolist()
+
+  dictionary1= dict(zip(idlist,predictionlist))
+
+  return dictionary1
+
+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
+feature-engine==1.6.2

sales_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7e870687b65519cff02bcf9d2ad5e5e6b123a0c2c72ad62fb67bb406a4ea83b8
+size 5812730