Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +1,16 @@
+# 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

@@ -0,0 +1,57 @@
+import requests
+import streamlit as st
+import pandas as pd
+
+st.title("Sales Forecaster")
+
+# Batch Prediction
+st.subheader("Predicting Sales")
+
+# Input fields for product data
+Product_Id = st.text_input("Product ID", value="FD6114")
+Product_Weight = st.number_input("Product Weight", min_value=0.0, max_value=22.0, format="%.2f")
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar", "reg"])
+Product_Allocated_Area = st.number_input("Product Allocated Area (Ratio of Total Area)  ", min_value=0.0, max_value=1.0, format="%.2f")
+Product_Type = st.selectbox("Type Of Product", ["Fruits and Vegetables", "Snack Foods", "Frozen Foods", "Dairy", "Household", "Baking Goods", "Canned", "Health and Hygiene", "Meat", "Soft Drinks", "Breads", "Hard Drinks","Others", "Starchy Foods", "Breakfast", "Seafood"])
+Product_MRP = st.number_input("Price of Product", min_value=0.0, format="%.2f")
+Store_Id = st.selectbox("Store ID", ["OUT004", "OUT001", "OUT003", "OUT002"])
+Store_Establishment_Year = st.number_input("Year of Store Establishment", min_value=1800, max_value=2025, value=2008)
+Store_Size = st.selectbox("Size of Store", ["High", "Medium", "Small"])
+Store_Location_City_Type = st.selectbox("City Tier of the Store", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Type of Store", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
+
+product_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_Id': Store_Id,
+    '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", type='primary'):
+    response = requests.post("https://Anil28053-Backend.hf.space/v1/customer", json=product_data)    # enter user name and space name before running the cell
+    if response.status_code == 200:
+        result = response.json()
+        sales_prediction = result["Prediction"]  # Extract only the value
+        st.write(f"Based on the information provided, the product with ID {Product_Id} is likely to sell {sales_prediction}.")
+    else:
+        st.error("Error in API request")
+
+# Batch Prediction
+st.subheader("Batch Prediction")
+
+file = st.file_uploader("Upload CSV file", type=["csv"])
+if file is not None:
+    if st.button("Predict for Batch", type='primary'):
+        response = requests.post("https://Anil28053-Backend.hf.space/v1/customerbatch", files={"file": file})    # enter user name and space name before running the cell
+        if response.status_code == 200:
+            result = response.json()
+            st.header("Batch Prediction Results")
+            st.write(result)
+        else:
+            st.error("Error in API request")

requirements.txt

@@ -0,0 +1,3 @@
+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2