Deploy Streamlit frontend

app.py

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+import os, pandas as pd, requests, streamlit as st
+
+API_BASE = (os.environ.get("API_BASE") or "https://Amitgupta2982-superkart-backend.hf.space").rstrip("/")
+
+st.set_page_config(page_title="SuperKart • Sales Price Predictor", page_icon="🛒", layout="wide")
+st.title("🛒 SuperKart — Sales Price Predictor")
+st.caption("Enter details to predict sales price, or upload a CSV to batch-score.")
+
+# ---- Health ----
+with st.sidebar:
+    st.header("⚙️ Backend")
+    api_base = st.text_input("API Base", value=API_BASE)
+    try:
+        r = requests.get(f"{api_base}/health", timeout=10)
+        st.success(f"Health: {r.status_code} • {r.text}")
+    except Exception as e:
+        st.error(f"Health check failed: {e}")
+
+tab_single, tab_batch = st.tabs(["🔮 Single Prediction", "📦 Batch Prediction"])
+
+# ---- Single ----
+with tab_single:
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        w = st.number_input("Product_Weight (kg)", 0.0, step=0.01, value=0.35)
+        sugar = st.number_input("Product_Sugar_Content (g)", 0.0, step=0.1, value=12.0)
+        area = st.number_input("Product_Allocated_Area (m²)", 0.0, step=0.1, value=1.2)
+    with col2:
+        ptype = st.selectbox("Product_Type", ["Snack","Grocery","Beverage","Personal Care","Other"])
+        mrp = st.number_input("Product_MRP", 0.0, step=1.0, value=199.0)
+        ssize = st.selectbox("Store_Size", ["Small","Medium","Large"], index=1)
+    with col3:
+        city = st.selectbox("Store_Location_City_Type", ["Tier 1","Tier 2","Tier 3"], index=1)
+        stype = st.selectbox("Store_Type", ["Supermarket","Hypermarket","Grocery","Convenience"])
+        year = st.number_input("Store_Establishment_Year", min_value=1900, max_value=2100, value=2015, step=1)
+
+    if st.button("Predict Price", type="primary"):
+        payload = {
+            "Product_Weight": w,
+            "Product_Sugar_Content": sugar,
+            "Product_Allocated_Area": area,
+            "Product_Type": ptype,
+            "Product_MRP": mrp,
+            "Store_Size": ssize,
+            "Store_Location_City_Type": city,
+            "Store_Type": stype,
+            "Store_Establishment_Year": year,
+        }
+        try:
+            r = requests.post(f"{api_base}/v1/salesprice", json=payload, timeout=20)
+            if r.status_code == 200:
+                st.success(f"Predicted Price: **{r.json().get('Predicted Price')}**")
+            else:
+                st.error(f"{r.status_code}: {r.text}")
+        except Exception as e:
+            st.error(str(e))
+
+# ---- Batch ----
+with tab_batch:
+    st.caption("CSV must include columns: Product_Weight, Product_Sugar_Content, Product_Allocated_Area, "
+               "Product_Type, Product_MRP, Store_Size, Store_Location_City_Type, Store_Type, Store_Establishment_Year")
+    sample = pd.DataFrame([{
+        "Product_Weight": 0.35,
+        "Product_Sugar_Content": 12.0,
+        "Product_Allocated_Area": 1.2,
+        "Product_Type": "Snack",
+        "Product_MRP": 199.0,
+        "Store_Size": "Medium",
+        "Store_Location_City_Type": "Tier 2",
+        "Store_Type": "Supermarket",
+        "Store_Establishment_Year": 2015,
+    }])
+    st.download_button("Download sample CSV", data=sample.to_csv(index=False).encode("utf-8"),
+                       file_name="superkart_sample.csv", mime="text/csv")
+
+    file = st.file_uploader("Upload CSV", type=["csv"])
+    if file and st.button("Run Batch Prediction", type="primary"):
+        try:
+            df = pd.read_csv(file)
+            st.write("Preview:", df.head())
+            csv_bytes = df.to_csv(index=False).encode("utf-8")
+            r = requests.post(f"{api_base}/v1/salespricebatch",
+                              files={"file": ("batch.csv", csv_bytes, "text/csv")},
+                              timeout=30)
+            if r.status_code == 200:
+                out = pd.DataFrame(r.json())
+                st.success("Done ✅")
+                st.dataframe(out, use_container_width=True)
+                st.download_button("Download predictions", out.to_csv(index=False).encode("utf-8"),
+                                   "predictions.csv", "text/csv")
+            else:
+                st.error(f"{r.status_code}: {r.text}")
+        except Exception as e:
+            st.error(str(e))
+
+st.markdown("---")
+st.caption("SuperKart • Streamlit frontend for the Sales Price Prediction API")
+

requirements.txt

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+
+streamlit==1.43.2
+pandas==2.2.2
+requests==2.32.3