Upload folder using huggingface_hub

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"]

app.py

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+import streamlit as st
+import pandas as pd
+import requests
+
+# Streamlit UI for SuperKart Sales Revenue Forecasting
+st.title("SuperKart Sales Revenue Forecasting App")
+st.write("This app predicts the total sales revenue for product-store combinations based on product and store features.")
+st.write("Adjust the values below to get a sales prediction.")
+
+# Collect user input using sliders and selectboxes
+col1, col2 = st.columns(2)
+
+with col1:
+    st.subheader("Product Features")
+    Product_Weight = st.slider("Product Weight", 5.0, 25.0, 12.5, 0.1)
+    Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+    Product_Allocated_Area = st.slider("Product Allocated Area (ratio)", 0.0, 0.5, 0.05, 0.01)
+    Product_Type = st.selectbox("Product Type", [
+        "Meat", "Snack Foods", "Hard Drinks", "Dairy", "Canned", "Soft Drinks",
+        "Health and Hygiene", "Baking Goods", "Bread", "Breakfast", "Frozen Foods",
+        "Fruits and Vegetables", "Household", "Seafood", "Starchy Foods", "Others"
+    ])
+    Product_MRP = st.slider("Product MRP (Maximum Retail Price)", 50.0, 300.0, 150.0, 1.0)
+
+with col2:
+    st.subheader("Store Features")
+    Store_Establishment_Year = st.slider("Store Establishment Year", 1985, 2015, 2000, 1)
+    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", [
+        "Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"
+    ])
+
+# Create input data dictionary
+input_data = {
+    '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_Establishment_Year': Store_Establishment_Year,
+    'Store_Size': Store_Size,
+    'Store_Location_City_Type': Store_Location_City_Type,
+    'Store_Type': Store_Type
+}
+
+if st.button("Predict Sales Revenue", type='primary'):
+    # Replace with your Hugging Face backend space URL
+    api_url = "https://<----user-name---->-<---repo name--->.hf.space/v1/sales"  # Enter user name and space name
+    try:
+        response = requests.post(api_url, json=input_data, timeout=30)
+        if response.status_code == 200:
+            result = response.json()
+            predicted_sales = result["Predicted_Sales_Total"]
+            st.success(f"💰 Predicted Sales Revenue: **${predicted_sales:,.2f}**")
+            st.info(f"This prediction is for a {Product_Type} product in a {Store_Size} {Store_Type} located in {Store_Location_City_Type}.")
+        else:
+            st.error(f"Error in API request: Status Code {response.status_code}")
+            st.error(f"Response: {response.text}")
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error connecting to API: {str(e)}")
+        st.info("Please ensure your backend API is deployed and the URL is correct.")
+
+# Batch Prediction
+st.subheader("Batch Prediction")
+st.write("Upload a CSV file with product and store features to get predictions for multiple combinations.")
+
+file = st.file_uploader("Upload CSV file", type=["csv"])
+if file is not None:
+    # Display preview of uploaded file
+    df_preview = pd.read_csv(file)
+    st.write("Preview of uploaded file:")
+    st.dataframe(df_preview.head())
+
+    if st.button("Predict for Batch", type='primary'):
+        # Replace with your Hugging Face backend space URL
+        api_url = "https://<----user-name---->-<---repo name--->.hf.space/v1/salesbatch"  # Enter user name and space name
+        try:
+            files = {"file": (file.name, file, "text/csv")}
+            response = requests.post(api_url, files=files, timeout=60)
+            if response.status_code == 200:
+                result = response.json()
+                result_df = pd.DataFrame(result)
+                st.header("Batch Prediction Results")
+                st.dataframe(result_df)
+
+                # Download button
+                csv = result_df.to_csv(index=False).encode('utf-8')
+                st.download_button(
+                    label="Download Predictions as CSV",
+                    data=csv,
+                    file_name="predictions.csv",
+                    mime="text/csv"
+                )
+            else:
+                st.error(f"Error in API request: Status Code {response.status_code}")
+                st.error(f"Response: {response.text}")
+        except requests.exceptions.RequestException as e:
+            st.error(f"Error connecting to API: {str(e)}")
+            st.info("Please ensure your backend API is deployed and the URL is correct.")

requirements.txt

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