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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
+
+# Streamlit runs on port 7860 by default in Hugging Face Spaces
+EXPOSE 7860
+
+# Define the command to run the Streamlit app on port 7860 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--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 SalesPrediction")
+
+# Collect user input for property features
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ['Low Sugar' 'Regular' 'No Sugar' 'reg'])
+Product_Weight = st.number_input("Product Weight", min_value=0, value=12.66)
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0,  value=0.027)
+Product_Type = st.selectbox("Product_Type", ['Frozen Foods' 'Dairy' 'Canned' 'Baking Goods' 'Health and Hygiene'
+ 'Snack Foods' 'Meat' 'Household' 'Hard Drinks' 'Fruits and Vegetables'
+ 'Breads' 'Soft Drinks' 'Breakfast' 'Others' 'Starchy Foods' 'Seafood'])
+Product_MRP = st.number_input("Product_MRP",value=117.08)
+Store_Establishment_Year = st.number_input("Store_Establishment_Year",value=2009)
+Store_Size = st.selectbox("Store_Size",['Medium' 'High' 'Small'])
+Store_Type = st.selectbox("Store_Type", ['Tier 2' 'Tier 1' 'Tier 3'])
+Store_Location_City_Type = st.selectbox("Store_Location_City_Type", ['Supermarket Type2' 'Departmental Store' 'Supermarket Type1' 'Food Mart'])
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'Product_Sugar_Content': Product_Sugar_Content,
+    'Product_Weight': Product_Weight,
+    'Product_Allocated_Area': Product_Allocated_Area,
+    'Product_Type': Product_Type,
+    'Product_MRP': Product_MRP,
+    'Store_Establishment_Year': Store_Establishment_Year,
+    'Store_Size': Store_Size,
+    'Store_Type': Store_Type,
+    'Store_Location_City_Type': Store_Location_City_Type
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://debrupa24-debrupa24/SuperKartSalesPredictionBackend.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    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")
+
+# Allow users to upload a CSV file for batch prediction
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
+
+# 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://debrupa24-SuperKartSalesPredictionBackend.hf.space/v1/salesBatch", files={"file": uploaded_file})  # Send file to Flask API
+        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