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app.py

@@ -1,42 +1,56 @@
-import streamlit as st
-import pandas as pd
-import requests
-
-# Set the title of the Streamlit app
-st.title("Superkart Forecast Revenue")
-
-# Section for online prediction
-st.subheader("Online Prediction")
-
-# Collect user input for property features
-product_weight = st.number_input("Product Weight", min_value=0)
-product_allocated_area = st.number_input("Product Allocated Area", min_value=0)
-product_mrp = st.number_input("Product MRP", min_value=0)
-
-product_sugar_content = st.Product_Sugar_Content("Product Sugar Content", ["Low Sugar","Regular","No Sugar","reg"])
-product_type = st.selectbox("Product Type", ["Fruits and Vegetables","Snack Foods","Frozen Foods","Dairy","Household","Baking Goods","Canned","Health and Hygiene","Meat","Soft Drinks","Breads","Hard Drinks","Others","Starchy Foods","Breakfast","Seafood"])
-
-store_size = st.selectbox("Store Size", ["Small","Medium","Large"])
-store_city = st.selectbox("Store Size", ["Tier 1","Tier 2","Tier 3"])
-store_type = st.selectbox("Store Type", ["Food Mart","Departmental Store","Supermarket Type1","Supermarket Type2"])
-
-# Convert user input into a DataFrame
-input_data = pd.DataFrame([{
-            'product_weight': product_weight,
-        'product_allocated_area': product_allocated_area,
-        'product_mrp': product_mrp,
-        'product_sugar_content': product_sugar_content,
-        'product_type': product_type,
-        'store_size': store_size,
-        'city_type': store_city,
-        'store_type': store_type
-}])
-
-# Make prediction when the "Predict" button is clicked
-if st.button("Predict"):
-    response = requests.post("https://shyamgoyal-ForecastRevenueBackend.hf.space/v1/revenue", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
-    if response.status_code == 200:
-        prediction = response.json()['Forecasted Revenue (in dollars)']
-        st.success(f"Forecasted Revenue Price (in dollars): {prediction}")
-    else:
-        st.error("Error making prediction.")
+# 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
+superkart_forecast_revenue = Flask("Superkart Forecast Revenue")
+
+# Load the trained machine learning model
+model = joblib.load("./backend_files/forecast_sales_prediction_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@forecast_revenue_api.get('/')
+def home():
+    """
+    This function handles GET requests to the root URL ('/') of the API.
+    It returns a simple welcome message.
+    """
+    return "Welcome to the Superkart Forecast Revenue API!"
+
+# Define an endpoint for single property prediction (POST request)
+@forecast_revenue_api.post('/v1/revenue')
+def forecast_revenue():
+    """
+    This function handles POST requests to the '/v1/revenue' endpoint.
+    It expects a JSON payload containing store details and returns
+    the predicted revenue as a JSON response.
+    """
+    # Get the JSON data from the request body
+    store_data = request.get_json()
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'product_weight': store_data['Product_Weight'],
+        'product_allocated_area': store_data['Product_Allocated_Area'],
+        'product_mrp': store_data['Product_MRP'],
+        'product_sugar_content': store_data['Product_Sugar_Content'],
+        'product_type': store_data['Product_Type'],
+        'store_size': store_data['Store_Size'],
+        'city_type': store_data['Store_Location_City_Type'],
+        'store_type': store_data['Store_Type']    
+        }
+
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get log_price)
+    forecast_revenue = model.predict(input_data)[0]
+
+    # Return the actual price
+    return jsonify({'Forecasted revenue (in dollars)': forecast_revenue})
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == '__main__':
+    superkart_forecast_revenue.run(debug=True)

requirements.txt

@@ -1,2 +1,11 @@
 pandas==2.2.2
-requests==2.28.1
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.32.3
+uvicorn[standard]
+streamlit==1.43.2