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 +1,82 @@
+import streamlit as st
+import pandas as pd
+import huggingface_hub
+from huggingface_hub import HfApi,hf_hub_download
+import joblib
+import os
+
+# Connect to HuggingFace Space using token from git secret
+print("Connecting to Huggingface...")
+try:
+    api = HfApi(token=os.getenv("HF_TOKEN"))
+    print("Connected..")
+except Exception as e:
+    print(f"Error connecting to HuggingFace Space:{e}")
+
+# Download the model from the Model Hub
+model_path = hf_hub_download(repo_id="supravab/Tourism_Package_Prediction", filename="tourism_package_prediction_modelv1.joblib")
+
+# Load the trained model
+print("Loading tourism_package_prediction model from Huggingface...")
+try:
+    model = joblib.load(model_path)
+    print("Model loaded successfully.")
+except FileNotFoundError:
+    print("Error: 'tourism_package_prediction_modelv1.joblib' not found. Please train and save the model first.")
+    model = None
+
+# Streamlit UI for Tourism Package Prediction
+print("Preparing Streamlit UI App for Tourism Package Prediction..")
+st.title("Tourism Package Prediction App")
+st.write("The Tourism Package Prediction App is an internal tool for the company, that predicts whether a customer purchase a tourist package.")
+st.write("Kindly enter the customer details to check whether they are likely to purchase.")
+
+# Collect user input
+Age = st.number_input("Age (Age of the customer)", min_value=15, max_value=100, value=30)
+Gender = st.selectbox("Gender (Gender of customer)", ["Male", "Female"])
+MaritalStatus = st.selectbox("MaritalStatus (Marital Status of customer)", ["Married", "Unmarried", "Divorced"])
+Occupation = st.selectbox("Occupation (Occupation of customer)", ["Salaried", "Small Business", "Large Business", "Free Lancer"])
+Designation = st.selectbox("Designation (Designation of customer)", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
+CityTier = st.selectbox("CityTier (city category based on living)", ["1", "2","3"])
+MonthlyIncome = st.number_input("MonthlyIncome (customer’s monthly income)", min_value=0.0, value=50000.0)
+Passport = st.selectbox("Has Passport?", ["Yes", "No"])
+OwnCar = st.selectbox("Has Own Car?", ["Yes", "No"])
+
+NumberOfPersonVisiting = st.number_input("NumberOfPersonVisiting (No of people accompanying the customer)", min_value=1, max_value=10, value=5)
+NumberOfChildrenVisiting = st.number_input("NumberOfChildrenVisiting (No of children accompanying the customer)", min_value=0, max_value=5, value=2)
+NumberOfTrips = st.number_input("NumberOfTrips (No of trips per year)", min_value=0, max_value=10, value=3)
+
+TypeofContact = st.selectbox("TypeofContact (Method by which customer was contacted)", ["Self Enquiry", "Company Invited"])
+ProductPitched = st.selectbox("ProductPitched (Type of product pitched)", ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"])
+DurationOfPitch = st.number_input("DurationOfPitch (Duration of the sales pitch)", min_value=0, max_value=100, value=20)
+NumberOfFollowups = st.number_input("NumberOfFollowups (Number of follow-ups by the salesperson)", min_value=0, max_value=10, value=2)
+PitchSatisfactionScore = st.number_input("PitchSatisfactionScore (Pitch satisfaction score given by customer)", min_value=0, max_value=10, value=5)
+PreferredPropertyStar = st.number_input("PreferredPropertyStar (Preferred rating given by customer)", min_value=1, max_value=5, value=2)
+
+# Assemble input into DataFrame
+input_data = pd.DataFrame([{
+    'Age': Age,
+    'Gender': Gender,
+    'MaritalStatus': MaritalStatus,
+    'Occupation': Occupation,
+    'Designation': Designation,
+    'CityTier': CityTier,
+    'MonthlyIncome': MonthlyIncome,
+    'Passport': 1 if Passport == "Yes" else 0,
+    'OwnCar': 1 if OwnCar == "Yes" else 0,
+    'NumberOfPersonVisiting': NumberOfPersonVisiting,
+    'NumberOfChildrenVisiting': NumberOfChildrenVisiting,
+    'NumberOfTrips': NumberOfTrips,    
+    'TypeofContact': TypeofContact,
+    'ProductPitched': ProductPitched,
+    'DurationOfPitch': DurationOfPitch,
+    'NumberOfFollowups': NumberOfFollowups,
+    'PitchSatisfactionScore': PitchSatisfactionScore,
+    'PreferredPropertyStar': PreferredPropertyStar
+}])
+
+if st.button("Predict Purchase"):
+    prediction = model.predict(input_data)[0]
+    result = "Purchase Yes" if prediction == 1 else "Purchase No"
+    st.subheader("Prediction Result:")
+    st.success(f"The model predicts: **{result}**")

requirements.txt

@@ -1,3 +1,8 @@
-altair
-pandas
-streamlit
+numpy==2.0.2
+pandas==2.2.2
+scikit-learn==1.6.1
+xgboost==3.1.2
+mlflow==3.0.1
+joblib==1.5.1
+huggingface_hub==0.32.6
+streamlit==1.43.2