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Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9-slim
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Define the command to run the Streamlit app on port 8501 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
+
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

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+import streamlit as st
+import requests
+import pandas as pd
+
+st.set_page_config(page_title="SuperKart Sales Prediction", page_icon="🛒", layout="centered")
+st.title("🛒 SuperKart Sales Prediction")
+
+# --- API base (set your HF Space URL here) ---
+api_url = st.text_input(
+    "API Base URL",
+    value="https://vinodcwanted-SuperKart.hf.space",
+    help="Your Flask API base (no trailing slash). Example: https://thiresh-rentalpricepredictionbackend.hf.space",
+)
+
+st.markdown("### Product Details")
+col1, col2 = st.columns(2)
+with col1:
+    product_weight = st.number_input("Product_Weight", min_value=0.0, value=12.5, step=0.1)
+    product_alloc_area = st.number_input("Product_Allocated_Area", min_value=0.0, value=0.05, step=0.001, format="%.3f")
+    product_mrp = st.number_input("Product_MRP", min_value=0.0, value=150.0, step=0.1)
+    product_id = st.text_input("Product_Id (optional)", value="", help="If provided, API derives product_categories from its prefix (FD/NC/DR).")
+with col2:
+    product_sugar_content = st.selectbox("Product_Sugar_Content", ["Low Sugar", "Regular", "No Sugar"])
+    product_type = st.selectbox(
+        "Product_Type",
+        [
+            "Frozen Foods","Dairy","Canned","Baking Goods","Health and Hygiene","Snack Foods","Meat",
+            "Household","Hard Drinks","Fruits and Vegetables","Breads","Soft Drinks","Breakfast",
+            "Others","Starchy Foods","Seafood"
+        ],
+    )
+    product_categories = st.selectbox(
+        "product_categories (optional)",
+        ["(leave blank)","FD","NC","DR"],
+        help="If left blank, API will derive from Product_Id (if provided)."
+    )
+
+st.markdown("### Store Details")
+col3, col4 = st.columns(2)
+with col3:
+    store_size = st.selectbox("Store_Size", ["Small", "Medium", "High"])
+    store_type = st.selectbox("Store_Type", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
+with col4:
+    store_city_type = st.selectbox("Store_Location_City_Type", ["Tier 1", "Tier 2", "Tier 3"])
+    store_est_year = st.number_input("Store_Establishment_Year", min_value=1900, max_value=2025, value=2005, step=1,
+                                     help="API will compute Establishment_age = 2025 - this year.")
+
+# Build payload (single JSON)
+payload = {
+    "Product_Weight": product_weight,
+    "Product_Sugar_Content": product_sugar_content,
+    "Product_Allocated_Area": product_alloc_area,
+    "Product_Type": product_type,
+    "Product_MRP": product_mrp,
+    "Store_Establishment_Year": int(store_est_year),
+    "Store_Size": store_size,
+    "Store_Location_City_Type": store_city_type,
+    "Store_Type": store_type,
+}
+
+# Optional fields
+if product_id.strip():
+    payload["Product_Id"] = product_id.strip()
+if product_categories != "(leave blank)":
+    payload["product_categories"] = product_categories
+
+st.markdown("---")
+if st.button("Predict"):
+    if not api_url.strip():
+        st.error("Please enter your API Base URL.")
+    else:
+        try:
+            endpoint = api_url.rstrip("/") + "/v1/sale"
+            with st.spinner("Contacting API..."):
+                resp = requests.post(endpoint, json=payload, timeout=30)
+            if resp.status_code == 200:
+                data = resp.json()
+                if "prediction" in data:
+                    st.success(f"Predicted Sales: **{data['prediction']}**")
+                else:
+                    st.warning(f"API responded without 'prediction' key:\n{data}")
+            else:
+                st.error(f"API error {resp.status_code}: {resp.text}")
+        except Exception as e:
+            st.error(f"Request failed: {e}")
+
+with st.expander("Show request payload"):
+    st.json(payload)

requirements.txt

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