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Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.10.11-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
+
+st.title("Super Kart Product Pricing Predictor")
+
+# Input fields for product and store data
+Product_Weight = st.number_input("Product Weight", min_value=0.0, value=10.00)
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"], value="Low Sugar")
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, value=100.00)
+Product_MRP = st.number_input("Product MRP", min_value=0.0, value=100.00)
+Product_Type = st.selectbox("Product Type", ["Baking Goods", "Breads", "Breakfest", "Canned", "Dairy", "Frozen Foods", "Fruits and Vegetables", "Hard Drinks", "Health and Hygiene", "Household", "Meat", "Others", "Seafood", "Snack Foods", "Soft Drinks", "Starchy Foods"], value="Baking Goods")
+Store_Size = st.selectbox("Store Size", ["Small", "Medium", "Large"], value="Small")
+Store_Location_City_Type = st.selectbox("Store Location City Type", ["Urban", "Suburban", "Rural"], value="Urban")
+Store_Type = st.selectbox("Store Type", ["Grocery", "Supermarket", "Hypermarket"], value="Grocery")
+Product_Id_char = st.text_input("Product Id", value="FD5075")
+Store_Age_Years = st.selectbox("Store Opening Year", [1987, 1988, 1999, 2009], value=1987)
+Product_Type_Category = st.selectbox("Product Type Category", ["Food", "Electronics", "Clothing"], value="Food")
+
+product_data = {
+    "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
+}
+
+if st.button("Predict", type='primary'):
+    response = requests.post("https://rpeltier-SuperKartPredictorBackend.hf.space/v1/predict", json=product_data)
+    if response.status_code == 200:
+        result = response.json()
+        predicted_sales = result["Sales"]
+        st.write(f"Predicted Product Store Sales Total: ${predicted_sales :,.2f}")
+    else:
+        st.error("Error in API request")

requirements.txt

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+requests==2.32.3
+streamlit==1.45.0