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app.py

@@ -1,46 +1,68 @@
-import streamlit as st
-import pandas as pd
-import requests
-
-# Set the title of the Streamlit app
-st.title("SuperKart sales Prediction")
-
-# Section for online prediction
-st.subheader("Online Prediction")
-
-# Collect user input for Sales features
-# Collect user input
-product_Weight=st.number_input("Product_Weight", min_value=1, value=30.00)
-product_Sugar_Content=st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular"])	
-product_Allocated_Area=st.number_input("Product Allocate Area", min_value=0.001, value=0.09)
-product_Type=st.selectbox("Product Type", ["meat", "snack foods", "hard drinks", "dairy", "canned", "soft drinks", "health and hygiene", "baking goods", "bread", "breakfast", "frozen foods", "fruits and vegetables", "household", "seafood", "starchy foods", "others"])	
-product_MRP=st.number_input("Product MRP", min_value=1, value=30.00)
-store_Id=st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
-store_Establishment_Year=st.number_input("Store Establishment year", min_value=1987, value=2009)
-store_Size=st.selectbox("Store Size", ["High", "Medium", "Small"])
-store_Location_City_Type=st.selectbox("Store location City", ["Tier1", "Tier2", "Tier3"])
-store_Type=st.selectbox("Store Type", ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2", "Food Mart"])
-
-# Convert user input into a DataFrame
-input_data = pd.DataFrame([{
-    'product_Weight': product_Weight,
-    'product_Sugar_Content': product_Sugar_Content,
-    'product_Allocated_Area': product_Allocated_Area,
-    'product_Type': product_Type,
-    'product_MRP': product_MRP,
-    'store_Id': store_Id,
-    'store_Establishment_Year': store_Establishment_Year,
-    'store_Size': store_Size,
-    'store_Location_City_Type': store_Location_City_Type,
-    'store_Type': store_Type
-}])
-
-# Make prediction when the "Predict" button is clicked
-if st.button("Predict"):
-    response = requests.post("https://LalithaShiva/skproject.hf.space/v1/sksales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
-    if response.status_code == 200:
-        prediction = response.json()['Predicted Price (in dollars)']
-        st.success(f"Predicted Rental 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_sales_predictor_api = Flask("Superkart sales Predictor")
+
+# Load the trained machine learning model
+model = joblib.load("superkart_price_prediction_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@superkart_sales_predictor_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 sales Prediction API!"
+
+# Define an endpoint for single sales prediction (POST request)
+@superkart_sales_predictor_api.post('/v1/sksales')
+def predict_sksales_price():
+    """
+    This function handles POST requests to the '/v1/sksales' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted rental price as a JSON response.
+    """
+    # Get the JSON data from the request body
+    sksales_data = request.get_json()
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'product_Weight': product_Weight,
+        'product_Sugar_Content': product_Sugar_Content,
+        'product_Allocated_Area': product_Allocated_Area,
+        'product_Type': product_Type,
+        'product_MRP': product_MRP,
+        'store_Id': store_Id,
+        'store_Establishment_Year': store_Establishment_Year,
+        'store_Size': store_Size,
+        'store_Location_City_Type': store_Location_City_Type,
+        'store_Type': store_Type
+    }
+
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get log_price)
+    predicted_sales_price = model.predict(input_data)[0]
+
+    # Calculate actual price
+    predicted_price = np.exp(predicted_sales_price)
+
+    # Convert predicted_price to Python float
+    predicted_price = round(float(predicted_price), 2)
+    # The conversion above is needed as we convert the model prediction (log price) to actual price using np.exp, which returns predictions as NumPy float32 values.
+    # When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error
+
+    # Return the actual price
+    return jsonify({'Predicted sales price (in dollars)': predicted_price})
+
+
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == '__main__':
+    superkart_sales_predictor_api.run(debug=True)

requirements.txt

@@ -1,2 +1,11 @@
 pandas==2.2.2
+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==2.28.1
+uvicorn[standard]
+streamlit==1.43.2