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=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -0,0 +1,110 @@
+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="viveksardey/tourism-package-prediction-model",
+    filename="tourism-package-prediction_model.joblib"
+)
+model = joblib.load(model_path)
+
+# -------------------------------
+# STREAMLIT APP
+# -------------------------------
+st.title("Tourism Package Purchase Prediction App")
+
+st.write("""
+This application predicts whether a customer is likely to purchase the **Tourism Package**
+offered by *Visit with Us*.
+
+Please enter the customer details below to get the prediction.
+""")
+
+# -------------------------------
+# USER INPUT FIELDS
+# -------------------------------
+Age = st.number_input("Customer Age", min_value=0, max_value=100, value=30)
+
+Gender = st.selectbox("Gender", ["Male", "Female"])
+
+TypeofContact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"])
+
+CityTier = st.selectbox("City Tier", [1, 2, 3])
+
+Occupation = st.selectbox(
+    "Occupation",
+    ["Salaried", "Self Employed", "Freelancer", "Company Owner", "Other"]
+)
+
+MaritalStatus = st.selectbox(
+    "Marital Status",
+    ["Single", "Married", "Divorced"]
+)
+
+ProductPitched = st.selectbox(
+    "Product Pitched",
+    ["Basic", "Deluxe", "Standard", "King", "Super Deluxe"]
+)
+
+Designation = st.selectbox(
+    "Designation",
+    ["Manager", "Executive", "Senior Manager", "AVP", "VP"]
+)
+
+MonthlyIncome = st.number_input("Monthly Income", min_value=0, value=50000)
+
+NumberOfTrips = st.number_input("Average Trips per Year", min_value=0, value=1)
+
+NumberOfPersonVisiting = st.number_input("Number of Persons Visiting", min_value=1, value=2)
+
+PreferredPropertyStar = st.selectbox("Preferred Hotel Star Rating", [1, 2, 3, 4, 5])
+
+NumberOfChildrenVisiting = st.number_input("Number of Children Visiting", min_value=0, value=0)
+
+Passport = st.selectbox("Passport Available?", [0, 1])
+
+OwnCar = st.selectbox("Owns a Car?", [0, 1])
+
+PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", 1, 5, 3)
+
+NumberOfFollowups = st.number_input("Number of Follow-ups", min_value=0, value=2)
+
+DurationOfPitch = st.number_input("Duration of Pitch (minutes)", min_value=0, value=15)
+
+# -------------------------------
+# CREATE INPUT DATAFRAME
+# -------------------------------
+input_data = pd.DataFrame([{
+    "Age": Age,
+    "Gender": Gender,
+    "TypeofContact": TypeofContact,
+    "CityTier": CityTier,
+    "Occupation": Occupation,
+    "MaritalStatus": MaritalStatus,
+    "NumberOfPersonVisiting": NumberOfPersonVisiting,
+    "PreferredPropertyStar": PreferredPropertyStar,
+    "NumberOfTrips": NumberOfTrips,
+    "Passport": Passport,
+    "OwnCar": OwnCar,
+    "NumberOfChildrenVisiting": NumberOfChildrenVisiting,
+    "Designation": Designation,
+    "MonthlyIncome": MonthlyIncome,
+    "PitchSatisfactionScore": PitchSatisfactionScore,
+    "ProductPitched": ProductPitched,
+    "NumberOfFollowups": NumberOfFollowups,
+    "DurationOfPitch": DurationOfPitch
+}])
+
+# -------------------------------
+# PREDICTION
+# -------------------------------
+if st.button("Predict Purchase Likelihood"):
+    prediction = model.predict(input_data)[0]
+    result = "Will Purchase Package" if prediction == 1 else "Will Not Purchase Package"
+
+    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