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import joblib
import pandas as pd
from flask import Flask, request, jsonify
# Initialize Flask app
sales_forecast_api = Flask("SuperKart Sales Forecast API")
# Load trained model (includes preprocessing pipeline)
model = joblib.load("best_random_forest.pkl")
# Home route
@sales_forecast_api.get('/')
def home():
return "Welcome to the SuperKart Sales Forecast API!"
# Predict for a single input
@sales_forecast_api.post('/v1/predict')
def predict_sales():
data = request.get_json()
# Convert JSON into DataFrame (1 row)
input_df = pd.DataFrame([data])
# Model prediction
prediction = model.predict(input_df).tolist()[0]
return jsonify({"Predicted_Sales_Total": prediction})
# Predict for a batch (CSV upload)
@sales_forecast_api.post('/v1/predict_batch')
def predict_sales_batch():
# Read uploaded CSV file
file = request.files['file']
input_data = pd.read_csv(file)
# Check for optional Product_Id column
if "Product_Id" in input_data.columns:
ids = input_data["Product_Id"].astype(str)
input_data = input_data.drop(columns=["Product_Id"])
else:
ids = input_data.index.astype(str) # fallback to row indices
# Make predictions
preds = model.predict(input_data).tolist()
# Map Product_Id (or row index) → prediction
results = dict(zip(ids, preds))
return jsonify(results)
# Run app
if __name__ == "__main__":
sales_forecast_api.run(debug=True)
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