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 +1,65 @@
+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="UncloudMe/Tourism-Project", filename="best_tourism_prediction_model_v1.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI for Machine Failure Prediction
+st.title("Tourism Package Buyer Prediction System")
+st.write("""
+This application predicts potential buyers, and enhances decision-making for marketing strategies.
+Please enter the sensor and configuration data below to get a prediction.
+""")
+
+# User input
+Age = st.number_input("Customer Age", min_value=18, max_value=100,  step=1)
+TypeofContact= st.selectbox("Type of Contact", ["Company Invited", "Self Enquiry"])
+CityTier = st.number_input("City Tier", min_value=1, max_value=3)
+DurationOfPitch = st.number_input("Duration Of Pitch", min_value=1, max_value=180)
+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=1, max_value=5)
+NumberOfFollowups = st.number_input("Number Of Followups", min_value=1, max_value=10)
+ProductPitched= st.selectbox("Product Pitched", ["Basic", "Deluxe","Standard","King","Super Deluxe"])
+PreferredPropertyStar = st.number_input("Preferred Property Star", min_value=3, max_value=5)
+MaritalStatus= st.selectbox("Marital Status", ["Single", "Marrried","Unmarrried","Divorced"])
+NumberOfTrips = st.number_input("Number Of Trips", min_value=0, max_value=50)
+Passport=st.number_input("Passport", min_value=0, max_value=1)
+PitchSatisfactionScore = st.number_input("Pitch Satisfaction Score", min_value=1, max_value=5)
+OwnCar = st.number_input("Own Car", min_value=0, max_value=1)
+NumberOfChildrenVisiting = st.number_input("Number Of Children Visiting", min_value=0, max_value=5, value=0)
+Designation= st.selectbox("Designation", ["Manager", "Senior Manager","Executive","VP","AVP"])
+MonthlyIncome = st.number_input("MonthlyIncome", min_value=0, max_value=100000)
+
+
+# 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 Customer Potential"):
+    prediction = model.predict(input_data)[0]
+    result = "A Potential Customer" if prediction == 1 else "Not a potential customer"
+    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