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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 requests
+import streamlit as st
+import pandas as pd
+
+# Set the title of the Streamlit app
+st.header("SuperKart Product Sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Product Sales Prediction")
+
+# Collect user input for product and store features
+Product_Weight=st.number_input("Product Weight")
+Product_Sugar_Content=st.selectbox("Product Sugar Content",["Low Sugar","Medium Sugar","High Sugar"])
+Product_Allocated_Area=st.number_input("Product Allocated Area")
+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")
+Store_Id=st.selectbox("Store Id",['OUT001', 'OUT002', 'OUT003', 'OUT004'])
+Store_Size=st.selectbox("Store Size",['Small', 'Medium', 'High'])
+Store_Location_City_Type=st.selectbox("Store Location City Type",['Tier 1', 'Tier 2', 'Tier 3'])
+Store_Type=st.selectbox("Store Type",['Supermarket Type2', 'Departmental Store', 'Supermarket Type1',
+       'Food Mart'])
+Store_Establishment_Year=st.number_input("Store Establishment Year",min_value=1980, max_value=2025, value=1987)
+
+# Convert user input into a DataFrame
+input_data=pd.DataFrame([{
+    'Product_Weight':Product_Weight,
+    'Product_Sugar_Content':Product_Sugar_Content,
+    'Product_Allocated_Area':Product_Allocated_Area,
+    'Product_Type':Product_Type,
+    'Product_MRP':Product_MRP,
+    'Store_Id':Store_Id,
+    'Store_Size':Store_Size,
+    'Store_Location_City_Type':Store_Location_City_Type,
+    'Store_Type':Store_Type,
+    'Store_Age':2025-Store_Establishment_Year
+}])
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+  response = requests.post("https://Parthi07-SuperKartProductPricePrediction.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])
+  if response.status_code == 200:
+    prediction = response.json()['Predicted Product Sales Price']
+    st.success(f"Predicted Product Sales Price: {prediction}")
+  else:
+    st.error(f"Error making prediction: {response.status_code}")
+
+
+# Section for batch prediction
+st.subheader("Batch Product Sales Prediction")
+
+# Allow users to upload a CSV file for batch prediction
+uploaded_file = st.file_uploader("Upload a CSV file for Batch Product Sales 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://Parthi07-SuperKartProductPricePrediction.hf.space/v1/salesbatch", files={'file': uploaded_file})
+    if response.status_code == 200:
+      prediction = response.json()
+      st.success("Batch Product Sales Prediction Successful!")
+      st.write(prediction)
+    else:
+      st.error(f"Error making batch prediction: {response.status_code}")

requirements.txt

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+pandas==2.2.2
+requests==2.32.3
+streamlit==1.43.2