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 +1,77 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# ------------------------------
+# Load model from Hugging Face Hub
+# ------------------------------
+model_path = hf_hub_download(
+    repo_id="Vignesh-vigu/Tourism-Package-Prediction",  # replace with your actual repo_id
+    filename="best_tourism_model_v1.joblib"
+)
+model = joblib.load(model_path)
+
+# ------------------------------
+# Streamlit UI
+# ------------------------------
+st.title("🧳 Tourism Wellness Package Prediction App")
+st.write("""
+This app predicts whether a customer is likely to purchase the new **Wellness Tourism Package**.
+Please fill in the details below:
+""")
+
+# ------------------------------
+# Input Form
+# ------------------------------
+age = st.number_input("Age", min_value=18, max_value=100, value=35)
+typeof_contact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
+city_tier = st.selectbox("City Tier", [1, 2, 3])
+duration_of_pitch = st.number_input("Duration of Pitch (minutes)", min_value=0, max_value=60, value=10)
+occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Free Lancer"])
+gender = st.radio("Gender", ["Male", "Female"])
+num_person_visiting = st.slider("Number of Persons Visiting", 1, 5, 2)
+num_followups = st.slider("Number of Followups", 0, 10, 2)
+product_pitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe"])
+preferred_property_star = st.selectbox("Preferred Property Star", [1, 2, 3, 4, 5])
+marital_status = st.selectbox("Marital Status", ["Single", "Married", "Divorced", "Unmarried"])
+num_trips = st.number_input("Number of Trips", min_value=0, max_value=50, value=2)
+passport = st.radio("Passport", [0, 1])
+pitch_satisfaction = st.slider("Pitch Satisfaction Score", 1, 5, 3)
+own_car = st.radio("Own Car", [0, 1])
+num_children_visiting = st.slider("Number of Children Visiting", 0, 5, 0)
+designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
+monthly_income = st.number_input("Monthly Income", min_value=1000, max_value=100000, value=20000)
+
+# ------------------------------
+# Prepare Input Data
+# ------------------------------
+input_data = pd.DataFrame([{
+    "Age": age,
+    "TypeofContact": typeof_contact,
+    "CityTier": city_tier,
+    "DurationOfPitch": duration_of_pitch,
+    "Occupation": occupation,
+    "Gender": gender,
+    "NumberOfPersonVisiting": num_person_visiting,
+    "NumberOfFollowups": num_followups,
+    "ProductPitched": product_pitched,
+    "PreferredPropertyStar": preferred_property_star,
+    "MaritalStatus": marital_status,
+    "NumberOfTrips": num_trips,
+    "Passport": passport,
+    "PitchSatisfactionScore": pitch_satisfaction,
+    "OwnCar": own_car,
+    "NumberOfChildrenVisiting": num_children_visiting,
+    "Designation": designation,
+    "MonthlyIncome": monthly_income
+}])
+
+# ------------------------------
+# Predict Button
+# ------------------------------
+if st.button("Predict"):
+    prediction = model.predict(input_data)[0]
+    result = "✅ Likely to Purchase Package" if prediction == 1 else "❌ Not Likely to Purchase"
+    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