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Dockerfile

@@ -1,20 +1,16 @@
-FROM python:3.13.5-slim
+# 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
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# 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
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# 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"]
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -0,0 +1,77 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# -------------------------
+# SuperKart Streamlit UI
+# -------------------------
+st.title("SuperKart Sales Revenue Predictor")
+
+st.markdown("""
+Use this app to predict the **expected sales revenue** for a given product
+based on its characteristics and store details.
+""")
+
+# -------------------------
+# Online Prediction Section
+# -------------------------
+st.subheader("Single Product Prediction")
+
+# --- Collect user inputs ---
+Product_Weight = st.number_input("Product Weight (in kg)", min_value=0.0, step=0.1)
+Product_Allocated_Area = st.number_input("Product Allocated Area (ratio to total area)", min_value=0.0, step=0.001)
+Product_MRP = st.number_input("Product MRP (Maximum Retail Price)", min_value=0.0, step=0.5)
+
+Product_Sugar_Content = st.selectbox(
+    "Product Sugar Content",
+    ["Low Sugar", "Regular", "No Sugar", "reg"]
+)
+
+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"
+    ]
+)
+
+Store_Size = st.selectbox("Store Size", ["Medium", "High", "Small"])
+
+Store_Location_City_Type = st.selectbox(
+    "Store Location City Type",
+    ["Tier 1", "Tier 2", "Tier 3"]
+)
+
+Store_Type = st.selectbox(
+    "Store Type",
+    ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"]
+)
+
+# --- Prepare input data ---
+input_data = pd.DataFrame([{
+    "Product_Weight": Product_Weight,
+    "Product_Allocated_Area": Product_Allocated_Area,
+    "Product_MRP": Product_MRP,
+    "Product_Sugar_Content": Product_Sugar_Content,
+    "Product_Type": Product_Type,
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Store_Type": Store_Type
+}])
+
+# --- API endpoint (update this to your HF Space URL or local host) ---
+API_URL = "https://dtapkir-SuperkartSalesPredictionFrontend.hf.space/v1/predict-sales"
+
+# --- Make prediction ---
+if st.button("Predict Sales Revenue"):
+    with st.spinner("Predicting..."):
+        response = requests.post(API_URL, json=input_data.to_dict(orient="records")[0])
+        if response.status_code == 200:
+            prediction = response.json().get('Predicted Sales Revenue (in dollars)', None)
+            if prediction:
+                st.success(f"Predicted Sales Revenue: **${prediction}**")
+            else:
+                st.warning("No prediction returned. Check API response.")
+        else:
+            st.error(f"Error: {response.status_code}. Could not connect to backend API.")

requirements.txt

@@ -1,3 +1,3 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2