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 +1,57 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the trained model
+model_path = hf_hub_download(repo_id="pal14/visitWithUs_classification-model", filename="best_classification_model_visitWithUs.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI
+st.title("Tourism Package Prediction")
+st.write("""
+This application predicts predicts whether a customer will purchase the newly introduced Wellness Tourism Package before contacting them.
+Please enter the app details below to get a prediction.
+""")
+
+# User input
+Age = st.number_input("Age", min_value=0.0, max_value=100.0)
+TypeofContact = st.selectbox("Contact Type", ["Self Enquiry", "Company Invited"])
+CityTier = st.selectbox("City Tier", [1, 2, 3])
+
+Occupation = st.selectbox("Occupation", ["Salaried", "Free Lancer", "Small Business", "Large Business"])
+Gender = st.selectbox("Gender", ["Male", "Female"])
+NumberOfPersonVisiting = st.number_input("Number of Person Visiting", min_value=0, max_value=100000000)
+PreferredPropertyStar = st.selectbox("Preferred Property Star", [3., 4., 5.])
+MaritalStatus = st.selectbox("Marital Status", ["Single", "Divorced", "Married", "Unmarried"])
+NumberOfTrips = st.number_input("Number of Trips", min_value=0.0, max_value=10000.0)
+Passport = st.selectbox("Passport", [0, 1])
+
+OwnCar = st.selectbox("Own Car", [0, 1])
+NumberOfChildrenVisiting = st.selectbox("Number of children visiting", [0., 1., 2., 3.])
+Designation = st.selectbox("Designation", ["Manager", "Executive", "Senior Manager", "AVP", "VP"])
+MonthlyIncome = st.number_input("Long Ads per Hour", min_value=0.0, max_value=1000000.)
+
+# Assemble input into DataFrame
+input_data = pd.DataFrame([{
+    'Age': Age,
+    'TypeofContact': TypeofContact,
+    'CityTier': CityTier,
+    'Occupation': Occupation,
+    'Gender': Gender,
+    'NumberOfPersonVisiting': NumberOfPersonVisiting,
+    'PreferredPropertyStar': PreferredPropertyStar,
+    'MaritalStatus': MaritalStatus,
+    'NumberOfTrips': NumberOfTrips,
+    'Passport': Passport,
+    'OwnCar': OwnCar,
+    'NumberOfChildrenVisiting': NumberOfChildrenVisiting,
+    'Designation': Designation,
+    'MonthlyIncome': MonthlyIncome,
+}])
+
+# Predict button
+if st.button("Predict"):
+    prediction = model.predict(input_data)[0]
+    st.subheader("Prediction Result:")
+    st.success(f"Package Taken: {prediction}")

requirements.txt

@@ -1,3 +1,7 @@
-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
+xgboost==2.1.4
+mlflow==3.0.1