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 +1,70 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the model from Hugging Face Hub
+model_path = hf_hub_download(
+    repo_id="Sandhya777/tourism_package_prediction_model1",
+    filename="best_tourism_package_prediction_v2.joblib"
+)
+model = joblib.load(model_path)
+
+# Streamlit UI for Insurance Charges Prediction
+st.title("Tourism Package Prediction App")
+st.write("""
+This application predicts the **Tourism Package Prediction** based on personal and lifestyle details.
+Please enter the required information below to get a prediction.
+""")
+
+# User input
+age= st.number_input("Age", min_value=18, max_value=100, value=30, step=1)
+typeofcontact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"])
+citytier = st.selectbox("City Tier", [1, 2, 3])
+durationofpitch = st.number_input("Duration of Pitch", min_value=1, max_value=100, value=10, step=1)
+occupation= st.selectbox("Occupation", ["Salaried", "Freelancer"])
+gender = st.selectbox("Gender", ["Male", "Female"])
+numberofpersonvisiting = st.number_input("Number of People Visiting", min_value=1, max_value=10, value=2, step=1)
+numberoffollowups = st.number_input("Number of Follow-ups", min_value=1, max_value=10, value=2, step=1)
+productpitched= st.selectbox("Product Pitched", ["Basic", "Deluxe","Standard","King","Super Deluxe"])
+preferredpropertystar= st.number_input("Preferred Property Star", min_value=1, max_value=5, value=3, step=1)
+maritalstatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
+numberoftrips = st.number_input("Number of Trips", min_value=1, max_value=10, value=2, step=1)
+passport = st.selectbox("Passport", [0, 1])
+pitchsatisfactionscore = st.number_input("Pitch Satisfaction Score", min_value=1, max_value=5, value=3, step=1)
+owncar = st.selectbox("Own Car", [0, 1])
+numberofchildrenvisiting = st.number_input("Number of Children Visiting", min_value=0, max_value=10, value=0, step=1)
+designation = st.selectbox("Designation", ["Executive", "Managerial", "Professional", "Other"])
+monthlyincome = st.number_input("Monthly Income", min_value=1000, max_value=100000, value=5000, step=100)
+
+# Assemble input into DataFrame
+input_data = pd.DataFrame([{
+    'Age': age,
+    'TypeofContact': typeofcontact,
+    'CityTier': citytier,
+    'DurationOfPitch': durationofpitch,
+    'Occupation': occupation,
+    'Gender': gender,
+    'NumberOfPersonVisiting': numberofpersonvisiting,
+    'NumberOfFollowups': numberoffollowups,
+    'ProductPitched': productpitched,
+    'PreferredPropertyStar': preferredpropertystar,
+    'MaritalStatus': maritalstatus,
+    'NumberOfTrips': numberoftrips,
+    'Passport': passport,
+    'PitchSatisfactionScore': pitchsatisfactionscore,
+    'OwnCar': owncar,
+    'NumberOfChildrenVisiting': numberofchildrenvisiting,
+    'Designation': designation,
+    'MonthlyIncome': monthlyincome
+}])
+
+# Set the classification threshold
+classification_threshold = 0.45
+
+# Predict button
+if st.button("Predict"):
+    prediction_proba = model.predict_proba(input_data)[0, 1]
+    prediction = (prediction_proba >= classification_threshold).astype(int)
+    result = "Package Taken" if prediction == 1 else "Package not Taken"
+    st.write(f"Based on the information provided, the customer is likely to {result}.")

requirements.txt

@@ -1,3 +1,6 @@
-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