app.py

# Import custom classes first
from model_classes import ProductTypeCategorizer

# 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
from huggingface_hub import hf_hub_download




# class ProductTypeCategorizer(BaseEstimator, TransformerMixin):
#     def __init__(self):
#         self.perishables = [
#             "Dairy",
#             "Meat",
#             "Fruits and Vegetables",
#             "Breakfast",
#             "Breads",
#             "Seafood",
#         ]
#     def fit(self, X, y=None):
#         return self
#     def transform(self, X):
#         X = X.copy()
#         X = pd.Series(X.squeeze())  # ensure 1D
#         return pd.DataFrame(
#             X.apply(lambda x: "Perishables" if x in self.perishables else "Non Perishables")
#         )



model_path = hf_hub_download(
    repo_id="PR118/SalesForecastModel-New",
    filename="superkart_sales_forecast_prediction_model_v1_0.joblib"
)


# Load the trained churn prediction model
model = joblib.load(model_path)

# Initialize Flask app with a name
superkart_api = Flask("Sales Forecast Predictor Model")



# Define a route for the home page
@superkart_api.get('/')
def home():
    return "Welcome to the Sales Forecast Prediction API!" #Complete the code to define a welcome message

# Define an endpoint to predict churn for a single customer
@superkart_api.post('/v1/predict')
def predict_sales():
    # Get JSON data from the request
    data = request.get_json()

    # Extract relevant customer features from the input data. The order of the column names matters.
    sample = {
        'Product_Weight': data['Product_Weight'],
        'Product_Sugar_Content': data['Product_Sugar_Content'],
        'Product_Allocated_Area': data['Product_Allocated_Area'],
        'Product_MRP': data['Product_MRP'],
        'Store_Size': data['Store_Size'],
        'Store_Location_City_Type': data['Store_Location_City_Type'],
        'Store_Type': data['Store_Type'],
        # 'Product_Id_char': data['Product_Id_char'],
        'Store_Age_Years': data['Store_Age_Years'],
        'Product_Type': data['Product_Type']
    }

    # Convert the extracted data into a DataFrame
    input_data = pd.DataFrame([sample])

    # Make a churn prediction using the trained model
    prediction = model.predict(input_data).tolist()[0]

    # Return the prediction as a JSON response
    return jsonify({'Sales': prediction})


# Define an endpoint for batch prediction (POST request)
@superkart_api.post('/v1/predictbatch')
def predict_sales_batch():
    """
    This function handles POST requests to the '/v1/predictbatch' 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 CSV file into a Pandas DataFrame
    input_data = pd.read_csv(file)

    # Make predictions for all properties in the DataFrame (get log_prices)
    predicted_sales = model.predict(input_data).tolist()

    # 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 app in debug mode
if __name__ == '__main__':
    superkart_api.run(debug=True)