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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 Uvicorn
+CMD ["uvicorn", "app:superkart_api", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import joblib
+import numpy as np
+import pandas as pd
+from flask import Flask, request, jsonify
+
+superkart_api = FastAPI("Superkart Sales Prediction")
+
+model = joblib.load('model_1.joblib')
+
+@superkart_api.get('/')
+def home():
+  return "Welcome to SuperKart Sales Prediction API!"
+
+@superkart_api.post('/v1/superkart_single')
+def salepred_single():
+  sales_data = request.get_json()
+
+  # Read input data
+  sample = {
+      'Product_Weight':sale_data['Product_Weight'],
+      'Product_Sugar_Content':sale_data['Product_Sugar_Content'],
+      'Product_Allocated_Area':sale_data['Product_Allocated_Area'],
+      'Product_Type':sale_data['Product_Type'],
+      'Product_MRP':sale_data['Product_MRP'],
+      'Store_Id':sale_data['Store_Id'],
+      'Store_Size':sale_data['Store_Size'],
+      'Store_Location_City_Type':sale_data['Store_Location_City_Type'],
+      'Store_Type':sale_data['Store_Type'],
+
+  }
+  input_data = pd.DataFrame([sample])
+
+  # Make predictions
+  predicted_sale = model.predict(input_data)[0]
+
+  # Create response
+  response = {'Store_Outlet':sample['Store_Id'],"Sale":round(float(predicted_sale), 2)}
+  return jsonify(response)
+
+@superkart_api.post('/v1/superkart_batch')
+def salepred_batch():
+    file = request.files['file']
+    print("File Received:", file.filename)
+
+    # Read input data
+    input_data = pd.read_csv(file)
+
+    # Make predictions
+    predicted_sale = model.predict(input_data).tolist()
+
+    # Add predictions to input data
+    input_data['Predicted_Sale'] = predicted_sale
+
+    # Group by Store_Id and sum the predicted sales
+    grouped_sales = input_data.groupby('Store_Id')['Predicted_Sale'].sum().to_dict()
+
+    # Create response
+    response = {
+        'store_sales': {store_id: round(float(sale), 2) for store_id, sale in grouped_sales.items()}
+    }
+    print("Final Response:", response)
+
+    return jsonify(response)
+
+
+
+
+if __name__=='__main__':
+  superkart_api.run()

model_1.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:12d5d4995153ea1ec62ffc53c9891a2893e3cd3582c14696d30a074e6822d385
+size 47878618

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+flask==3.1.2
+joblib==1.4.2
+xgboost==2.1.4
+streamlit==1.49.1
+requests==2.32.3
+uvicorn[standard]