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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:sales_forecast_api"]

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
+sales_forecast_api = Flask("Sales Forecast Prediction API ")
+
+# Load the trained churn prediction model
+model = joblib.load("sales_forecast_model_v1_0.joblib")
+
+# Define a route for the home page
+@sales_forecast_api.get('/')
+def home():
+    return "Welcome to the Sales Forecast Prediction API!"
+
+# Define an endpoint to predict churn for a single customer
+@sales_forecast_api.post('/v1/product')
+def predict_sales_forecast():
+    # Get JSON data from the request
+    product_data = request.get_json()
+
+    # Extract relevant customer features from the input data
+    sample = {
+        'Product_Weight': product_data['Product_Weight'],
+        'Product_Sugar_Content': product_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': product_data['Product_Allocated_Area'],
+        'Product_Type': product_data['Product_Type'],
+        'Product_MRP': product_data['Product_MRP'],
+        'Store_Id': product_data['Store_Id'],
+        'Store_Establishment_Year': product_data['Store_Establishment_Year'],
+        'Store_Size': product_data['Store_Size'],
+        'Store_Location_City_Type': product_data['Store_Location_City_Type'],
+        'Store_Type': product_data['Store_Type']
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get sales_forecast)
+    predicted_log_price = model.predict(input_data)[0]
+
+    # Calculate actual price
+    predicted_price = np.exp(predicted_log_price)
+
+    # Map prediction result to a human-readable label
+    predicted_price = round(float(predicted_price), 2)
+
+    # Return the prediction as a JSON response
+    return jsonify({'Sales Forecast (in dollars)': predicted_price})
+
+# Define an endpoint to predict churn for a batch of customers
+@sales_forecast_api.post('/v1/productbatch')
+def predict_sales_forecast_batch():
+    # Get the uploaded CSV file from the request
+    file = request.files['file']
+
+    # Read the CSV file into a Pandas DataFrame
+    input_data = pd.read_csv(file)
+
+    # Make predictions for all properties in the DataFrame (get log_prices)
+    predicted_log_prices = model.predict(input_data).tolist()
+
+    # Calculate actual prices
+    predicted_sales = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_prices]
+
+    # Create a dictionary of predictions with property IDs as keys
+    product_ids = input_data['Product_ID'].tolist()  # Assuming 'id' is the property ID column
+    output_dict = dict(zip(product_ids, predicted_sales))  # Use actual prices
+
+    # Return the predictions dictionary as a JSON response
+    return output_dict
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == '__main__':
+    sales_forecast_api.run(debug=True)

requirements.txt

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+numpy==2.0.2
+pandas==2.2.2
+scikit-learn==1.6.1
+seaborn==0.13.2
+joblib==1.4.2
+xgboost==2.1.4
+requests==2.32.3
+huggingface_hub==0.30.1
+Flask==3.0.3

sales_forecast_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c24befbc23d8e5538fd746cf4710b8edf1ea5cab6bb129398a9a67e6a3133af6
+size 141790218