Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,23 @@
-FROM python:3.13.5-slim
+# 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
 
-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
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+COPY --chown=user . $HOME/app
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# 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

@@ -0,0 +1,44 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the model
+model_path = hf_hub_download(repo_id="ccwizard/PIMA-Diabetes-Prediction", filename="best_pima_diabetes_model_v1.joblib")                                       # enter the Hugging Face username here
+model = joblib.load(model_path)
+
+# Streamlit UI for Machine Failure Prediction
+st.title("PIMA Diabetes Prediction App")
+st.write("""
+This application predicts the likelihood of a patient having diabetes based on their health attributes.
+Please enter the sensor and configuration data below to get a prediction.
+""")
+
+# User inputs
+preg = st.number_input("Number of Pregnancies", min_value=0, max_value=20, value=1)
+plas = st.number_input("Plasma Glucose Concentration", min_value=0, max_value=300, value=120)
+pres = st.number_input("Diastolic Blood Pressure (mm Hg)", min_value=0, max_value=200, value=70)
+skin = st.number_input("Triceps Skinfold Thickness (mm)", min_value=0, max_value=100, value=20)
+test = st.number_input("2-Hour Serum Insulin (mu U/ml)", min_value=0, max_value=900, value=80)
+mass = st.number_input("Body Mass Index (BMI)", min_value=0.0, max_value=70.0, value=25.0, step=0.1)
+pedi = st.number_input("Diabetes Pedigree Function", min_value=0.0, max_value=2.5, value=0.5, step=0.01)
+age = st.number_input("Age", min_value=1, max_value=120, value=30)
+
+# Assemble input into DataFrame
+input_data = pd.DataFrame([{
+    'preg': preg,
+    'plas': plas,
+    'pres': pres,
+    'skin': skin,
+    'test': test,
+    'mass': mass,
+    'pedi': pedi,
+    'age': age
+}])
+
+# Prediction button
+if st.button("Predict Diabetes"):
+    prediction = model.predict(input_data)[0]
+    result = "Diabetic" if prediction == 1 else "Non-Diabetic"
+    st.subheader("Prediction Result:")
+    st.success(f"The model predicts: **{result}**")

requirements.txt

@@ -1,3 +1,5 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+huggingface_hub==0.32.6
+streamlit==1.43.2
+joblib==1.5.1
+scikit-learn==1.6.0