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 +1,80 @@
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+from huggingface_hub import hf_hub_download
+import joblib
+
+st.set_page_config(page_title="Visit With Us — Tourism Package Predictor", page_icon="🧳", layout="centered")
+
+# Download the model from the Model Hub
+model_path = hf_hub_download(repo_id="Abhilashu/tourism-model", filename="best_tourism_model_v1.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+
+st.title("Visit with us Tourism Package Purchase — Prediction")
+st.write("Fill the details and click **Predict**. The model estimates the probability that a customer will buy the Tourism Package.")
+
+with st.form("input_form"):
+    col1, col2 = st.columns(2)
+
+    with col1:
+        Age = st.number_input("Age", min_value=18, max_value=90, value=35, step=1)
+        CityTier = st.number_input("CityTier (1=metro, 2, 3)", min_value=1, max_value=3, value=1, step=1)
+        DurationOfPitch = st.number_input("DurationOfPitch (minutes)", min_value=0.0, value=10.0, step=1.0)
+        NumberOfPersonVisiting = st.number_input("NumberOfPersonVisiting", min_value=1.0, value=3.0, step=1.0)
+        NumberOfFollowups = st.number_input("NumberOfFollowups", min_value=0.0, value=3.0, step=1.0)
+        PreferredPropertyStar = st.number_input("PreferredPropertyStar (1-5)", min_value=1.0, max_value=5.0, value=3.0, step=1.0)
+
+    with col2:
+        NumberOfTrips = st.number_input("NumberOfTrips (per year)", min_value=0.0, value=2.0, step=1.0)
+        Passport = st.selectbox("Passport", options=[0,1], index=1)
+        PitchSatisfactionScore = st.number_input("PitchSatisfactionScore (1-5)", min_value=1.0, max_value=5.0, value=3.0, step=1.0)
+        OwnCar = st.selectbox("OwnCar", options=[0,1], index=0)
+        NumberOfChildrenVisiting = st.number_input("NumberOfChildrenVisiting (under 5)", min_value=0.0, value=0.0, step=1.0)
+        MonthlyIncome = st.number_input("MonthlyIncome", min_value=0.0, value=25000.0, step=500.0)
+
+    TypeofContact = st.selectbox("TypeofContact", ["Company Invited", "Self Enquiry"])
+    Occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Free Lancer"])
+    Gender = st.selectbox("Gender", ["Male", "Female"])
+    ProductPitched = st.selectbox("ProductPitched", ["Basic", "Deluxe", "Standard"])
+    MaritalStatus = st.selectbox("MaritalStatus", ["Single", "Married", "Divorced", "Unmarried"])
+    Designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager"])
+
+    submitted = st.form_submit_button("Predict")
+
+# Set the classification threshold
+classification_threshold = 0.45
+
+if submitted:
+    # NOTE: include ALL training features
+    row = {
+        "Age": float(Age),
+        "CityTier": float(CityTier),
+        "DurationOfPitch": float(DurationOfPitch),
+        "TypeofContact": str(TypeofContact).strip(),   # <-- added
+        "Occupation": str(Occupation).strip(),
+        "Gender": str(Gender).strip(),
+        "NumberOfPersonVisiting": float(NumberOfPersonVisiting),
+        "NumberOfFollowups": float(NumberOfFollowups),
+        "ProductPitched": str(ProductPitched).strip(),
+        "PreferredPropertyStar": float(PreferredPropertyStar),
+        "MaritalStatus": str(MaritalStatus).strip(),
+        "NumberOfTrips": float(NumberOfTrips),
+        "Passport": float(Passport),
+        "PitchSatisfactionScore": float(PitchSatisfactionScore),
+        "OwnCar": float(OwnCar),
+        "NumberOfChildrenVisiting": float(NumberOfChildrenVisiting),
+        "Designation": str(Designation).strip(),
+        "MonthlyIncome": float(MonthlyIncome),
+    }
+
+    X = pd.DataFrame([row])
+
+    proba = model.predict_proba(X)[:, 1][0]
+    pred  = int(proba >= classification_threshold)
+
+    st.subheader("Result")
+    st.metric("Predicted probability of purchase", f"{proba:.3f}")
+    st.write("Prediction:", "**Yes**" if pred==1 else "**No**")

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