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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 Forecaster", page_icon="🛒", layout="wide")
+
+# Backend API URL — points to your deployed Flask backend
+BACKEND_URL = "https://codedfortamara-superkart-backend.hf.space/predict"
+
+st.title("🛒 SuperKart Sales Revenue Forecaster")
+st.markdown("Predict sales revenue for any product-store combination using our trained ML model.")
+st.divider()
+
+col1, col2 = st.columns(2)
+
+with col1:
+    st.subheader("📦 Product Details")
+    product_weight = st.number_input("Product Weight (kg)", min_value=1.0, max_value=25.0, value=12.0, step=0.1)
+    product_sugar_content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+    product_allocated_area = st.slider("Allocated Display Area (ratio)", min_value=0.0, max_value=0.30, value=0.05, step=0.005, format="%.3f")
+    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=30.0, max_value=270.0, value=140.0, step=1.0)
+    product_category = st.selectbox("Product Category", ["FD", "NC", "DR"],
+        format_func=lambda x: {"FD": "FD – Food", "NC": "NC – Non-Consumable", "DR": "DR – Drinks"}[x])
+
+with col2:
+    st.subheader("🏬 Store Details")
+    store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+    store_location = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
+    store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
+    store_age = st.number_input("Store Age (years)", min_value=1, max_value=50, value=20, step=1)
+
+st.divider()
+
+if st.button("🔮 Predict Sales Revenue", type="primary", use_container_width=True):
+    payload = {
+        "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,
+        "Store_Type": store_type,
+        "Store_Age": store_age,
+        "Product_Category": product_category,
+    }
+    try:
+        response = requests.post(BACKEND_URL, json=payload, timeout=30)
+        if response.status_code == 200:
+            prediction = response.json()["predictions"][0]
+            st.success(f"### 💰 Predicted Sales Revenue: ₹{prediction:,.2f}")
+            st.markdown("#### Input Summary")
+            st.dataframe(pd.DataFrame([payload]), use_container_width=True)
+        else:
+            st.error(f"API Error: {response.json().get('error', 'Unknown error')}")
+    except requests.exceptions.ConnectionError:
+        st.error("Could not connect to the backend. Check if the backend space is running.")
+    except Exception as e:
+        st.error(f"Error: {str(e)}")
+
+st.divider()
+st.subheader("📊 Batch Prediction")
+uploaded_file = st.file_uploader("Upload CSV for batch predictions", type=["csv"])
+
+if uploaded_file is not None:
+    batch_df = pd.read_csv(uploaded_file)
+    st.dataframe(batch_df.head(), use_container_width=True)
+    if st.button("Run Batch Prediction", use_container_width=True):
+        try:
+            response = requests.post(BACKEND_URL, json=batch_df.to_dict(orient='records'), timeout=60)
+            if response.status_code == 200:
+                batch_df["Predicted_Sales"] = response.json()["predictions"]
+                st.success(f"Predictions generated for {len(batch_df)} records!")
+                st.dataframe(batch_df, use_container_width=True)
+                st.download_button("Download Predictions", batch_df.to_csv(index=False),
+                                   "superkart_predictions.csv", "text/csv")
+            else:
+                st.error(f"API Error: {response.json().get('error', 'Unknown error')}")
+        except Exception as e:
+            st.error(f"Error: {str(e)}")

requirements.txt

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+streamlit==1.45.1
+requests==2.32.4
+pandas==2.2.2