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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 single prediction
+st.subheader("Single Product-Store Prediction")
+
+# Collect user input for product and store features
+product_weight = st.number_input("Product Weight", min_value=0.1, max_value=20.0, value=12.65, step=0.1)
+product_allocated_area = st.number_input("Product Allocated Area Ratio", min_value=0.0, max_value=1.0, value=0.07, step=0.01)
+product_mrp = st.number_input("Product MRP", min_value=10, max_value=200, value=147, step=1)
+product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+product_type = st.selectbox("Product Type", [
+    "Fruits and Vegetables", "Household", "Snack Foods", "Meat", 
+    "Hard Drinks", "Dairy", "Canned", "Soft Drinks", 
+    "Health and Hygiene", "Baking Goods", "Bread", "Breakfast",
+    "Frozen Foods", "Seafood", "Starchy Foods", "Others"
+])
+store_size = st.selectbox("Store Size", ["High", "Medium", "Low"])
+store_location_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2", "Food Mart"])
+store_age = st.number_input("Store Age (years)", min_value=1, max_value=50, value=10, step=1)
+product_category_code = st.number_input("Product Category Code", min_value=1, max_value=100, value=10, step=1)
+
+# Convert user input into the format expected by the API
+input_data = {
+    'Product_Weight': float(product_weight),
+    'Product_Allocated_Area': float(product_allocated_area),
+    'Product_MRP': int(product_mrp),
+    'Product_Sugar_Content': product_sugar_content,
+    'Product_Type': product_type,
+    'Store_Size': store_size,
+    'Store_Location_City_Type': store_location_city_type,
+    'Store_Type': store_type,
+    'Store_Age': int(store_age),
+    'Product_Category_Code': int(product_category_code)
+}
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict Sales"):
+    try:
+        # Replace with your actual backend URL
+        backend_url = "https://simnid-superkartsalesbackend.hf.space/v1/predict"
+        response = requests.post(backend_url, json=input_data)
+        
+        if response.status_code == 200:
+            prediction = response.json()['Predicted Sales Total']
+            st.success(f"Predicted Sales Total: ${prediction:,.2f}")
+        else:
+            st.error(f"Error making prediction. Status code: {response.status_code}")
+            st.write(response.text)
+    except Exception as e:
+        st.error(f"Error connecting to API: {e}")
+
+# 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"):
+        try:
+            # Replace with your actual backend batch URL
+            batch_backend_url = "https://simnid-superkartsalesbackend.hf.space/v1/predictbatch"
+            response = requests.post(batch_backend_url, files={"file": uploaded_file})
+            
+            if response.status_code == 200:
+                predictions = response.json()
+                st.success("Batch predictions completed!")
+                
+                # Display predictions in a nice format
+                predictions_df = pd.DataFrame.from_dict(predictions, orient='index', columns=['Predicted Sales'])
+                st.dataframe(predictions_df)
+                
+                # Add download button
+                csv = predictions_df.to_csv(index=True)
+                st.download_button(
+                    label="Download Predictions as CSV",
+                    data=csv,
+                    file_name="superkart_predictions.csv",
+                    mime="text/csv"
+                )
+            else:
+                st.error(f"Error making batch prediction. Status code: {response.status_code}")
+        except Exception as e:
+            st.error(f"Error connecting to API: {e}")
+
+# Add some information about the app
+st.sidebar.header("About")
+st.sidebar.info("""
+This app predicts sales totals for SuperKart products using a machine learning model.
+- **Single Prediction**: Enter details for one product-store combination
+- **Batch Prediction**: Upload a CSV file with multiple records
+""")

requirements.txt

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