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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 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 joblib
+import numpy as np
+
+# UI Title and Subtitle
+st.title("🛒 SuperKart Sales Forecasting App")
+st.write("This tool predicts **product-level revenue** in a specific store using historical and categorical inputs.")
+
+# UI for Input Features
+st.subheader("Enter Product & Store Details:")
+
+# Categorical Inputs
+product_type = st.selectbox("Product Type", [
+    "Meat", "Snack Foods", "Soft Drinks", "Dairy", "Household", "Fruits and Vegetables", 
+    "Frozen Foods", "Breakfast", "Baking Goods", "Health and Hygiene", "Starchy Foods"
+])
+
+store_type = st.selectbox("Store Type", [
+    "Supermarket Type1", "Supermarket Type2", "Supermarket Type3", "Grocery Store"
+])
+
+city_type = st.selectbox("City Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+sugar_content = st.selectbox("Product Sugar Content", ["No Sugar", "Low Sugar", "Regular"])
+
+# Numerical Inputs
+product_weight = st.number_input("Product Weight (kg)", min_value=0.0, max_value=50.0, value=10.0, step=0.1)
+product_mrp = st.number_input("Product MRP", min_value=0.0, max_value=1000.0, value=200.0, step=1.0)
+allocated_area = st.number_input("Allocated Display Area (0-1)", min_value=0.0, max_value=1.0, value=0.2, step=0.01)
+store_est_year = st.number_input("Store Establishment Year", min_value=1950, max_value=2025, value=2010)
+
+# Convert to DataFrame
+input_data = pd.DataFrame({
+    'Product_Type': [product_type],
+    'Store_Type': [store_type],
+    'Store_Location_City_Type': [city_type],
+    'Store_Size': [store_size],
+    'Product_Sugar_Content': [sugar_content],
+    'Product_Weight': [product_weight],
+    'Product_MRP': [product_mrp],
+    'Product_Allocated_Area': [allocated_area],
+    'Store_Establishment_Year': [store_est_year],
+})
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://huggingface.co/spaces/omoral02/RevenuePredictionBackend/v1/predict", 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 Revenue (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://huggingface.co/spaces/omoral02/RevenuePredictionBackend/v1/batch", 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
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+streamlit==1.43.2