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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 pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("SuperKart sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+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_Id = st.selectbox("Store_Id", [ "OUT001", "OUT002","OUT003"] )
+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"] )
+Product_Weight = st.number_input("Product_Weight", min_value=1, step=1, value=1)
+Product_MRP = st.number_input("Product_MRP", min_value=1, step=1, value=1)
+
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'Product_Sugar_Content': Product_Sugar_Content,
+    'Product_Type': Product_Type,
+    'Store_Id': Store_Id,
+    'Store_Size': Store_Size,
+    'Store_Type': Store_Type,
+    'Product_Weight': Product_Weight,
+    'Product_MRP': Product_MRP
+    }])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+  response = requests.post("https://huggingface.co/spaces/mdsalmon159/SalesPredictionBackend.hf.space/v1/sales", json=input_data.to_dict(orient='records' ) [0])
+  if response.status_code == 200:
+    prediction = response. json() ['Predicted Price (in dollars)']
+    st.success(f"Predicted Rental Price (in dollars): {prediction}")
+  else:
+    st.error("Error making prediction.")
+
+# Section for batch prediction
+st.subheader("Batch Prediction")
+
+# Make batch prediction when the "Predict Batch" button is clicked
+if uploaded_file is not None:
+  if st.button("Predict Batch"):
+    response = requests.post("https://huggingface.co/spaces/mdsalmon159/SalesPredictionBackend.hf.space/v1/sales_batch", files={"file": uploaded_file}) # S
+    if response.status_code == 200:
+      predictions = response. json( )
+      st.success("Batch predictions completed!")
+      st.write(predictions) # Display the predictions
+    else:
+      st.error("Error making batch prediction.")

requirements.txt

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+pandas == 2.2.2
+requests == 2.28.1
+streamlit == 1.43.2