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 +1,75 @@
+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="best_tourism_prediction_model_v1.joblib", filename="best_tourism_prediction_model_v1.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI for Machine Failure Prediction
+st.title("Tour Pack Purchase Prediction App")
+st.write("""
+This application predicts whether a customer will purchase the newly introduced Wellness Tourism Package or not.
+Please enter the customer details below to get a prediction.
+""")
+
+# User input
+st.subheader("Customer Information")
+
+Age = st.number_input("Age", min_value=18, max_value=100, value=35, step=1)
+Gender = st.selectbox("Gender", ["Male", "Female"])
+MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Unmarried", "Divorced"])
+Occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free lancer"])
+MonthlyIncome = st.number_input("Monthly Income ($)", min_value=0, max_value=1000000, value=50000, step=1000)
+Designation = st.text_input("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
+
+st.subheader("Location & Family Details")
+
+CityTier = st.selectbox("City Tier", [1, 2, 3])
+NumberOfPersonVisiting = st.number_input("Number of Persons Visiting", min_value=1, max_value=10, value=2, step=1)
+NumberOfChildrenVisiting = st.number_input("Number of Children Visiting", min_value=0, max_value=5, value=0, step=1)
+OwnCar = st.number_input("Owns a Car", min_value=0, max_value=1, value=0, step=1)
+
+st.subheader("Travel History")
+
+NumberOfTrips = st.number_input("Number of Trips (Annual)", min_value=0, max_value=50, value=3, step=1)
+Passport = st.number_input("Passport", min_value=0, max_value=1, value=0, step=1)
+PreferredPropertyStar = st.slider("Preferred Hotel Star Rating", min_value=3, max_value=5, value=3, step=1)
+
+st.subheader("Sales Interaction Details")
+
+TypeofContact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"])
+ProductPitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"])
+DurationOfPitch = st.number_input("Duration of Pitch (minutes)", min_value=1, max_value=150, value=15, step=1)
+NumberOfFollowups = st.number_input("Number of Follow-ups", min_value=1, max_value=20, value=3, step=1)
+PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", min_value=1, max_value=5, value=3, step=1)
+
+# Assemble input into DataFrame
+input_data = pd.DataFrame([{
+    'Age': Age,
+    'TypeofContact': TypeofContact,
+    'CityTier': CityTier,
+    'DurationOfPitch': DurationOfPitch,
+    'Occupation': Occupation,
+    'Gender': Gender,
+    'NumberOfPersonVisiting': NumberOfPersonVisiting,
+    'NumberOfFollowups': NumberOfFollowups,
+    'ProductPitched': ProductPitched,
+    'PreferredPropertyStar': PreferredPropertyStar,
+    'MaritalStatus': MaritalStatus,
+    'NumberOfTrips': NumberOfTrips,
+    'Passport': Passport,
+    'PitchSatisfactionScore': PitchSatisfactionScore,
+    'OwnCar': OwnCar,
+    'NumberOfChildrenVisiting': NumberOfChildrenVisiting,
+    'Designation': Designation,
+    'MonthlyIncome': MonthlyIncome
+}])
+
+
+if st.button("Predict Failure"):
+    prediction = model.predict(input_data)[0]
+    result = "Tour Pack Taken" if prediction == 1 else "Tour Pack Not Taken"
+    st.subheader("Prediction Result:")
+    st.success(f"The model predicts: **{result}**")

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