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 +1,97 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download the model from the Model Hub
+model_path = hf_hub_download(repo_id="anithajk/tourism_model", filename="best_tourism_model.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+# Streamlit UI for Tourism Package Purchase Prediction
+st.title("Tourism Package Purchase Prediction App")
+st.write("Fill the details below to predict whether the customer will purchase the package.")
+
+# Collect user input
+# -------------------------------------------------------
+# USER INPUT SECTION
+# -------------------------------------------------------
+
+Age = st.number_input(
+    "Age (Customer's age in years)",
+    min_value=18, max_value=80, value=30
+)
+
+DurationOfPitch = st.number_input(
+    "Duration Of Pitch (minutes)",
+    min_value=0, max_value=120, value=15
+)
+
+NumberOfFollowups = st.number_input(
+    "Number of Follow-ups",
+    min_value=0, max_value=10, value=2
+)
+
+MonthlyIncome = st.number_input(
+    "Monthly Income",
+    min_value=1000, max_value=300000, value=50000
+)
+
+NumberOfChildrenVisiting = st.number_input(
+    "Number Of Children Visiting",
+    min_value=0, max_value=5, value=1
+)
+
+Passport = st.selectbox("Passport (Does customer have a passport?)", ["Yes", "No"])
+OwnCar = st.selectbox("Own Car (Does customer own a car?)", ["Yes", "No"])
+
+TypeofContact = st.selectbox(
+    "Type of Contact",
+    ["Self Enquiry", "Company Invited"]
+)
+
+Occupation = st.selectbox(
+    "Occupation",
+    ["Salaried", "Small Business", "Large Business", "Student", "Free Lancer"]
+)
+
+Gender = st.selectbox(
+    "Gender",
+    ["Male", "Female"]
+)
+
+Designation = st.selectbox(
+    "Designation",
+    ["Executive", "Manager", "Senior Manager", "AVP", "VP"]
+)
+
+
+# Convert categorical inputs to match model training
+# -------------------------------------------------------
+# 2. CREATE INPUT DATAFRAME
+# -------------------------------------------------------
+input_data = pd.DataFrame({
+    "Age": [Age],
+    "DurationOfPitch": [DurationOfPitch],
+    "NumberOfFollowups": [NumberOfFollowups],
+    "MonthlyIncome": [MonthlyIncome],
+    "NumberOfChildrenVisiting": [NumberOfChildrenVisiting],
+    "Passport": [1 if Passport == "Yes" else 0],
+    "OwnCar": [1 if OwnCar == "Yes" else 0],
+    "TypeofContact": [TypeofContact],
+    "Occupation": [Occupation],
+    "Gender": [Gender],
+    "Designation": [Designation]
+})
+
+st.write("### Input Summary")
+st.dataframe(input_data)
+
+# Predict button
+if st.button("Predict"):
+    prediction = model.predict(input_data)[0]
+
+    if prediction == 1:
+        st.success("The customer is **LIKELY** to purchase the tourism package!")
+    else:
+        st.warning("The customer is **NOT likely** to purchase the package.")