Add SuperKart Streamlit frontend deployment files

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 8501
+
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app.py

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+
+# Streamlit frontend that collects product/store details and calls the Flask backend API
+
+import streamlit as st
+import requests
+
+# Backend URL: within the shared Docker network, containers reach each other
+# by container name. Defaults to the container name used when running the
+# backend container (see deployment instructions).
+BACKEND_URL = os.environ.get("BACKEND_URL", "http://superkart-backend:7860")
+
+st.set_page_config(page_title="SuperKart Sales Forecasting", layout="centered")
+st.title("SuperKart Sales Forecasting")
+st.write("Enter the product and store details below to forecast sales revenue.")
+
+# ----- Input widgets -----
+product_weight = st.number_input("Product Weight", min_value=0.0, value=12.66, step=0.1)
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+product_allocated_area = st.number_input(
+    "Product Allocated Area", min_value=0.0, max_value=1.0, value=0.027, step=0.001, format="%.3f"
+)
+product_mrp = st.number_input("Product MRP", min_value=0.0, value=117.08, step=0.1)
+store_size = st.selectbox("Store Size", ["High", "Medium", "Small"])
+store_location_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox(
+    "Store Type",
+    ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3", "Food Mart"],
+)
+product_id_char = st.selectbox("Product Id Prefix", ["FD", "DR", "NC"])
+store_age_years = st.number_input("Store Age (Years)", min_value=0, value=16, step=1)
+product_type_category = st.selectbox("Product Type Category", ["Perishables", "Non Perishables"])
+
+if st.button("Predict Sales"):
+    payload = {
+        "Product_Weight": product_weight,
+        "Product_Sugar_Content": product_sugar_content,
+        "Product_Allocated_Area": product_allocated_area,
+        "Product_MRP": product_mrp,
+        "Store_Size": store_size,
+        "Store_Location_City_Type": store_location_city_type,
+        "Store_Type": store_type,
+        "Product_Id_char": product_id_char,
+        "Store_Age_Years": store_age_years,
+        "Product_Type_Category": product_type_category,
+    }
+    try:
+        response = requests.post(f"{BACKEND_URL}/v1/predict", json=payload)
+        response.raise_for_status()
+        prediction = response.json()["predicted_Product_Store_Sales_Total"]
+        st.success(f"Predicted Sales: {prediction}")
+    except Exception as e:
+        st.error(f"Error while calling the backend API: {e}")

requirements.txt

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+streamlit==1.38.0
+requests==2.32.4