Upload app.py with huggingface_hub

app.py

@@ -1,36 +1,38 @@
-import requests
-import streamlit as st
-import pandas as pd
-
-st.title("🛒 SuperKart Sales Predictor")
-
-# Collect inputs
-Product_Weight = st.number_input("Product Weight", 0.0, 50.0, 12.5)
-Product_Sugar_Content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
-Product_Allocated_Area = st.number_input("Allocated Area", 0.0, 1.0, 0.25)
-Product_Type = st.text_input("Product Type", "Snack foods")
-Product_MRP = st.number_input("MRP", 0.0, 500.0, 200.0)
-Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
-Store_Location_City_Type = st.selectbox("City Type", ["Tier 1", "Tier 2", "Tier 3"])
-Store_Type = st.text_input("Store Type", "Supermarket Type1")
-
-if st.button("Predict"):
-    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_Size": Store_Size,
-        "Store_Location_City_Type": Store_Location_City_Type,
-        "Store_Type": Store_Type
-    }]
-
-    url = "https://akhilraja-superkart-flask-api.hf.space/predict"
-    response = requests.post(url, json=sample)
-
-    if response.status_code == 200:
-        result = response.json()
-        st.success(f"Predicted Sales: {result['predictions'][0]:.2f}")
-    else:
-        st.error(f"Error: {response.text}")
+import streamlit as st
+import requests
+
+st.title("🛒 SuperKart Sales Predictor")
+
+# Sidebar inputs
+Product_Weight = st.sidebar.number_input("Product Weight", min_value=0.0, max_value=100.0, value=10.0)
+Product_Sugar_Content = st.sidebar.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+Product_Allocated_Area = st.sidebar.number_input("Allocated Area", min_value=0.0, max_value=10.0, value=0.5)
+Product_Type = st.sidebar.text_input("Product Type", "Snack foods")
+Product_MRP = st.sidebar.number_input("MRP", min_value=0.0, max_value=1000.0, value=100.0)
+Store_Size = st.sidebar.selectbox("Store Size", ["Small", "Medium", "High"])
+Store_Location_City_Type = st.sidebar.selectbox("City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.sidebar.text_input("Store Type", "Supermarket Type1")
+
+if st.button("Predict Sales"):
+    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_Size": Store_Size,
+        "Store_Location_City_Type": Store_Location_City_Type,
+        "Store_Type": Store_Type
+    }]
+
+    url = "https://akhilraja-superkart-flask-api.hf.space/predict"
+    try:
+        response = requests.post(url, json=sample)
+        response.raise_for_status()
+        prediction = response.json().get("predictions", [None])[0]
+        if prediction is not None:
+            st.success(f"Predicted Sales: {prediction:.2f}")
+        else:
+            st.error("No prediction returned.")
+    except Exception as e:
+        st.error(f"Error: {e}")