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README.md

@@ -1,5 +1,8 @@
----
-sdk: streamlit
 title: SuperKartPredictionFrontend
+emoji: 🏢
 colorFrom: indigo
----
+colorTo: purple
+sdk: streamlit
+sdk_version: streamlit==1.43.2 # Added the sdk_version field
+app_file: app.py
+pinned: false

app.py

@@ -1,3 +1,69 @@
 import streamlit as st
-st.title("✅ Streamlit is running")
-st.title("✅ Set sdk to streamlit")
+import pandas as pd
+import numpy as np
+import requests as requests # Import requests library
+
+st.write("✅ App started - top of script reached")
+
+st.title("Frontend Test OK ✅")
+'''
+
+# Streamlit UI for Price Prediction
+st.title("SuperKart Revenue Prediction App")
+st.write("This tool predicts the Revenue for SuperKart store based on the details.")
+
+st.subheader("Enter the input details:")
+
+# Collect user input
+product_weight = st.number_input("Product_Weight", min_value=4.0, max_value=22.0, value=10.0)
+product_sugar_content = st.selectbox("Product_Sugar_Content", ["Low Sugar","Regular","No Sugar","reg"])
+product_allocated_area = st.number_input("Product_Allocated_Area", min_value=0.0040000, max_value=0.298000, value=0.1)
+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"])
+product_mrp = st.number_input("Product_MRP", min_value=31.0, max_value=266.0, value=100.0)
+store_id = st.selectbox("Store_Id", ["OUT004","OUT001","OUT003","OUT002"])
+store_age = st.number_input("Store_Age", min_value=16, max_value=38, value=20)
+store_size = st.selectbox("Store_Size", ["Medium","High","Small"])
+store_location_city_type = st.selectbox("Store_Location_City_Type", ["Tier 2","Tier 1","Tier 3"])
+store_type = st.selectbox("Store_Type", ["Supermarket Type2","Supermarket Type1","Departmental Store","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_age': store_age,
+    'store_size': store_size,
+    'store_location_city_type': store_location_city_type,
+    'store_type': store_type
+}])
+
+st.write("✅ Before API call")
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://data2aihub-SuperKartPredictionBackend.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()['Predicted Price (in dollars)']
+        st.success(f"Predicted Rental Price (in dollars): {prediction}")
+    else:
+        st.error("Error making prediction.")
+
+title: SuperKartPredictionFrontend
+emoji: 🏢
+colorFrom: indigo
+colorTo: purple
+sdk: streamlit
+app_file: app.py
+pinned: false
+
+title: SuperKartPredictionFrontend
+emoji: 🏢
+colorFrom: indigo
+colorTo: purple
+sdk: streamlit
+app_file: app.py
+pinned: false
+