Upload folder using huggingface_hub

Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9
+
+# 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
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+# 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
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download the model from the Model Hub
+model_path = hf_hub_download(repo_id="Anusha3/superkart-model", filename="best_superkart_model_v1.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+
+# Streamlit UI for Customer Churn Prediction
+st.title("SuperKart Package Prediction")
+st.write("Fill the customer details below to predict if they'll purchase a travel package")
+
+Product_Weight = st.slider("Product Weight (kg)", 0.5, 50.0, 10.0)
+
+Product_Sugar_Content = st.selectbox(
+    "Product Sugar Content",
+    ["Low Sugar", "Regular", "No Sugar"]
+)
+
+Product_Allocated_Area = st.slider(
+    "Product Allocated Area (sq.ft)",
+    0.1, 50.0, 5.0
+)
+
+Product_Type = st.selectbox(
+    "Product Type",
+    [
+        "Dairy", "Soft Drinks", "Meat", "Fruits and Vegetables",
+        "Household", "Baking Goods", "Snack Foods", "Frozen Foods",
+        "Breakfast", "Health and Hygiene"
+    ]
+)
+
+Product_MRP = st.number_input(
+    "Product MRP",
+    min_value=5.0,
+    max_value=1000.0,
+    value=120.0
+)
+
+Store_Id = st.selectbox(
+    "Store ID",
+    ["OUT001", "OUT002", "OUT003", "OUT004", "OUT005"]
+)
+
+Store_Establishment_Year = st.slider(
+    "Store Establishment Year",
+    1985, 2022, 2010
+)
+
+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",
+    ["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"]
+)
+
+# ----------------------------
+# Prepare input data (IMPORTANT)
+# ----------------------------
+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_Establishment_Year": Store_Establishment_Year,
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Store_Type": Store_Type
+}])
+
+
+
+# Set the classification threshold
+classification_threshold = 0.45
+
+# Predict button
+if st.button("Predict"):
+    sales = model.predict(input_data)[0]
+
+    result = f"is expected to generate total sales of ₹ {sales:,.2f}"
+
+    st.write(f"Prediction: Store {result}")

requirements.txt

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+pandas==2.2.2
+huggingface_hub==0.32.6
+streamlit==1.43.2
+joblib==1.5.1
+scikit-learn==1.6.0
+xgboost==2.1.4
+mlflow==3.0.1