Upload app.py

app.py

@@ -1,35 +1,36 @@
-import streamlit as st
 import requests
+import streamlit as st
 import pandas as pd
 
-# Backend API endpoint
-BACKEND_URL = "https://AkhilRaja-superkart-flask-api.hf.space/predict"
-
-st.set_page_config(page_title="SuperKart Predictor", page_icon="🛒", layout="wide")
+st.title("🛒 SuperKart Sales Predictor")
 
-st.title("🛒 SuperKart Predictor")
-st.markdown("Enter values to get predictions from the Random Forest model.")
-
-# Example input fields (replace with your dataset's actual features)
-feature1 = st.number_input("Feature 1", value=0.0)
-feature2 = st.number_input("Feature 2", value=0.0)
-feature3 = st.number_input("Feature 3", value=0.0)
+# 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"):
-    # Create input payload
-    data = pd.DataFrame([{
-        "feature1": feature1,
-        "feature2": feature2,
-        "feature3": feature3
-    }])
+    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)
 
-    # Send request to backend
-    try:
-        response = requests.post(BACKEND_URL, json=data.to_dict(orient="records"))
-        if response.status_code == 200:
-            result = response.json()
-            st.success(f"✅ Prediction: {result['predictions']}")
-        else:
-            st.error(f"❌ Error: {response.text}")
-    except Exception as e:
-        st.error(f"🚨 Failed to connect to backend: {e}")
+    if response.status_code == 200:
+        result = response.json()
+        st.success(f"Predicted Sales: {result['predictions'][0]:.2f}")
+    else:
+        st.error(f"Error: {response.text}")