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app.py

@@ -1,95 +1,66 @@
-# 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
-super_kart_sales_predictor_api = Flask("Super Kart Sales Predictor")
-
-# Load the trained machine learning model
-model = joblib.load("super_kart_sales_prediction_model_v1_0.joblib")
-
-# Define a route for the home page (GET request)
-@super_kart_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 super kart sales Prediction API!"
-
-# Define an endpoint for single property prediction (POST request)
-@super_kart_sales_predictor_api.post('/v1/kart')
-def predict_kart_sales():
-    """
-    This function handles POST requests to the '/v1/rental' 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
-    property_data = request.get_json()
-
-    # Extract relevant features from the JSON data
-    sample = {
-        'Product_Type': property_data['Product_Type'],
-        'Product_Weight': property_data['Product_Weight'],
-        'Product_MRP': property_data['Product_MRP'],
-        'Store_Type': property_data['Store_Type'],
-        'Store_Size': property_data['Store_Size'],
-        'Store_Location_City_Type': property_data['Store_Location_City_Type'],
-        'Product_Sugar_Content': property_data['Product_Sugar_Content'],
-
-        'Store_Id': property_data['Store_Id'],
-
-        'Product_Allocated_Area': property_data['Product_Allocated_Area'],
-        'Store_Establishment_Year': property_data['Store_Establishment_Year']
-    }
-
-    # Convert the extracted data into a Pandas DataFrame
-    input_data = pd.DataFrame([sample])
-
-    # Make prediction 
-    predicted_sales = model.predict(input_data)[0]
-
-
-
-    # Convert predicted_price to Python float
-    predicted_sales = round(float(predicted_sales), 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 (in dollars)': predicted_sales})
-
-
-# Define an endpoint for batch prediction (POST request)
-@super_kart_sales_predictor_api.post('/v1/salesbatch')
-def predict_rental_price_batch():
-    """
-    This function handles POST requests to the '/v1/rentalbatch' 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_log_sales = model.predict(input_data).tolist()
-
-    # Calculate actual prices
-    predicted_sales = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_sales]
-
-    # Create a dictionary of predictions with property IDs as keys
-    property_ids = input_data['id'].tolist()  # Assuming 'id' is the property ID column
-    output_dict = dict(zip(property_ids, predicted_sales))  # Use actual prices
-
-    # Return the predictions dictionary as a JSON response
-    return output_dict
-
-# Run the Flask application in debug mode if this script is executed directly
-if __name__ == '__main__':
-    rental_price_predictor_api.run(debug=True)
+import streamlit as st
+import pandas as pd
+import requests
+
+model_root_url = "https://bala-ai-KartSalesPredictionBackend.hf.space/v1/kart"  # Base URL of the deployed Flask API on Hugging Face Spaces
+
+
+# Set the title of the Streamlit app
+st.title("Super Kart Sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+Product_Sugar_Content = st.selectbox("Room Type", ["Entire home/apt", "Private room", "Shared room"])
+Product_Type = st.number_input("Accommodates (Number of guests)", min_value=1, value=2)
+Store_Establishment_Year = st.number_input("Bathrooms", min_value=1, step=1, value=2)
+Store_Location_City_Type = st.selectbox("Cancellation Policy (kind of cancellation policy)", ["strict", "flexible", "moderate"])
+Store_Id = st.selectbox("Cleaning Fee Charged?", ["True", "False"])
+Product_MRP = st.selectbox("Instantly Bookable?", ["False", "True"])
+Product_Weight = st.number_input("Review Score Rating", min_value=0.1, max_value=100.0, step=1.0, value=90.0)
+Store_Size = st.number_input("Bedrooms", min_value=0, step=1, value=1)
+Product_Allocated_Area = st.number_input("Area", min_value=0.1, step=1, value=1)
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+
+  "Product_Sugar_Content": Product_Sugar_Content,
+  "Product_Type": Product_Type,
+  "Store_Establishment_Year": Store_Establishment_Year,
+  "Store_Location_City_Type": Store_Location_City_Type,
+  "Store_Id": Store_Id,
+  "Product_MRP": Product_MRP,
+  "Product_Weight": Product_Weight,
+  "Store_Size": Store_Size,
+  "Store_Type" : Store_Type,
+  "Product_Allocated_Area" : Product_Allocated_Area
+
+
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post(model_root_url, 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.")
+
+# Section for batch prediction
+st.subheader("Batch Prediction")
+
+# Allow users to upload a CSV file for batch prediction
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
+
+# Make batch prediction when the "Predict Batch" button is clicked
+if uploaded_file is not None:
+    if st.button("Predict Batch"):
+        response = requests.post("https://<username>-<repo_id>.hf.space/v1/rentalbatch", files={"file": uploaded_file})  # Send file to Flask API
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success("Batch predictions completed!")
+            st.write(predictions)  # Display the predictions
+        else:
+            st.error("Error making batch prediction.")

requirements.txt

@@ -1,11 +1,2 @@
 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