Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,16 @@
-FROM python:3.13.5-slim
+# 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
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# 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
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# 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"]
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -0,0 +1,63 @@
+import requests
+import streamlit as st
+import pandas as pd
+
+st.title("Ftontend Prediction")
+
+# Batch Prediction
+st.subheader("Online Prediction")
+
+# Input fields for data entry (default values = median from your description)
+ID = st.number_input("Customer ID", min_value=1, max_value=5000, value=2500)
+Age = st.number_input("Age", min_value=18, max_value=100, value=45)
+Experience = st.number_input("Experience (Years)", min_value=-5, max_value=50, value=20)
+Income = st.number_input("Annual Income (in $000)", min_value=0, max_value=300, value=64)
+ZIPCode = st.number_input("ZIP Code", min_value=90000, max_value=99999, value=93437)
+Family = st.selectbox("Family Members", [1, 2, 3, 4], index=1)
+CCAvg = st.number_input("Credit Card Avg Monthly Spend", min_value=0.0, max_value=10.0, value=1.5)
+Education = st.selectbox("Education Level", [1, 2, 3], index=1)
+Mortgage = st.number_input("Mortgage Amount", min_value=0, max_value=1000, value=0)
+Securities_Account = st.selectbox("Has Securities Account?", [0, 1], index=0)
+CD_Account = st.selectbox("Has CD Account?", [0, 1], index=0)
+Online = st.selectbox("Uses Online Banking?", [0, 1], index=1)
+CreditCard = st.selectbox("Has Credit Card?", [0, 1], index=0)
+
+# Dictionary for model input
+user_input_data = {
+    'ID': ID,
+    'Age': Age,
+    'Experience': Experience,
+    'Income': Income,
+    'ZIPCode': ZIPCode,
+    'Family': Family,
+    'CCAvg': CCAvg,
+    'Education': Education,
+    'Mortgage': Mortgage,
+    'Securities_Account': Securities_Account,
+    'CD_Account': CD_Account,
+    'Online': Online,
+    'CreditCard': CreditCard
+}
+
+if st.button("Predict", type='primary'):
+    response = requests.post("https://kjdeka-test-backend.hf.space/v1/dijakbn", json=user_input_data)    # enter user name and backend space name before running the cell
+    if response.status_code == 200:
+        result = response.json()
+        frontend_prediction = result["Prediction"]  # Extract only the value
+        st.write(f"Prediction is {frontend_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://kjdeka-test-backend.hf.space/v1/dijakbnbatch", files={"file": file})    # enter user name and backend 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

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