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Dockerfile

@@ -1,7 +1,7 @@
 FROM python:3.9-slim
 
 # Set the working directory inside the container
-WORKDIR /app
+WORKDIR /superKartApi
 
 # Copy all files from the current directory to the container's working directory
 COPY . .
@@ -12,5 +12,5 @@ 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:app"]
+# - `app:superKartApi`: Runs the Flask app (assuming `superKartApi.py` contains the Flask instance named `superKartApi`)
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:superKartApi"]

superKartApi.py

@@ -0,0 +1,100 @@
+import joblib
+import pandas as pd
+import xgboost as xgb
+from xgboost import XGBRegressor
+from flask import Flask, request, jsonify
+
+# Initialize Flask app with a name
+superKartApi = Flask("Super Kart Sales Predictor")
+
+# Load the trained churn prediction model
+model = joblib.load("superkart_prediction_model_v1_0.joblib")
+
+# Define a route for the home page
+@superKartApi.get('/')
+def home():
+    return "Welcome to the Superkart Sales Forecast API"
+
+# Define an endpoint to predict churn for a single customer
+@superKartApi.post('/v1/product')
+def predict_forecast():
+    """
+    This function handles POST requests to the '/v1/product' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted rental price as a JSON response.
+    """
+
+    # Get JSON data from the request
+    superKar_data = request.get_json()
+
+    # Extract relevant customer features from the input data
+    sample = {
+        'ProductWeight': superKar_data['ProductWeight'],
+        'ProductSugarContent': superKar_data['ProductSugarContent'],
+        'ProductAllocatedArea': superKar_data['ProductAllocatedArea'],
+        'ProductType': superKar_data['ProductType'],
+        'ProductMRP': superKar_data['ProductMRP'],
+        'StoreId': superKar_data['StoreId'],
+        'StoreEstablishmentYear': superKar_data['StoreEstablishmentYear'],
+        'StoreSize': superKar_data['StoreSize'],
+        'StoreLocationCityType': superKar_data['StoreLocationCityType'],
+        'StoreType': superKar_data['StoreType']
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make a churn prediction using the trained model
+    predicted_sales_value = model.predict(input_data).tolist()[0]
+
+    # Calculate actual price
+    predicted_sales = np.exp(predicted_sales_value)
+
+    # Convert predicted_price to Python float
+    predicted_sales = round(float(predicted_sales), 2)
+
+    # Return the actual price
+    return jsonify({'Predicted Sales': predicted_sales})
+
+    # # Map prediction result to a human-readable label
+    # prediction_label = "churn" if prediction == 1 else "not churn"
+
+    # # Return the prediction as a JSON response
+    # return jsonify({'Prediction': prediction_label})
+
+# Define an endpoint to predict churn for a batch of customers
+@superKartApi.post('/v1/superKartbatch')
+def predict_churn_batch():
+
+    """
+    This function handles POST requests to the '/v1/superKartbatch' endpoint.
+    It expects a CSV file containing property details for multiple properties
+    and returns the predicted rental prices as a dictionary in the JSON response.
+    """
+
+    # Get the uploaded CSV file from the request
+    file = request.files['file']
+
+    # Read the file into a DataFrame
+    input_data = pd.read_csv(file)
+
+
+    # Make predictions for all properties in the DataFrame (get log_sales)
+    predicted_log_sales = model.predict(input_data).tolist()
+
+    # Calculate actual sales
+    predicted_sales = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_sales]
+
+    # # Make predictions for the batch data and convert raw predictions into a readable format
+    # predictions = [
+    #     'Churn' if x == 1
+    #     else "Not Churn"
+    #     for x in model.predict(input_data.drop("ProductId",axis=1)).tolist()
+    # ]
+
+    # Create a dictionary of predictions with property IDs as keys
+    product_id_list = input_data.ProductId.values.tolist()
+    output_dict = dict(zip(product_id_list, predicted_sales))
+
+    # Return the predictions dictionary as a JSON response
+    return output_dict