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 +1,68 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the model
+model_path = hf_hub_download(repo_id="vijayendras/Tourism-Package-Prediction", filename="tourism_package_prediction_model_v1.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI for Machine Failure Prediction
+st.title("Tourism Package Prediction App")
+st.write("""
+This application predicts the likelihood of a machine failing based on its operational parameters.
+Please enter the sensor and configuration data below to get a prediction.
+""")
+
+# User input
+age = st.number_input("Age (in years)", min_value=18, max_value=100, value=25)
+typeofContract = st.selectbox("Type of Contract", ["Self Enquiry", "Company Invited"])
+cityTier = st.number_input("City Tier", min_value=1, max_value=3, value=1)
+durationOfPitch = st.number_input("Duration Of Pitch", min_value=5, max_value=150, value=25)
+occupation = st.selectbox("Occupation", ["Salaried", "Small Business", 'Free Lancer', 'Large Business'])
+gender = st.selectbox("Gender", ["Male", "Female"])
+numberOfPersonVisiting = st.number_input("Number Of Person Visiting", min_value=1, max_value=6, value=1)
+numberOfFollowups = st.number_input("Number Of Followups", min_value=1, max_value=10, value=1)
+ProductPitched = st.selectbox("ProductPitched", ["Basic", "Standard", 'Deluxe', 'King', 'Super Deluxe'])
+preferredPropertyStar = st.number_input("Preferred Property Star", min_value=1, max_value=5, value=1)
+maritalStatus = st.selectbox("Marital Status", ["Married", "Single", 'Divorced','Unmarried'])
+numberOfTrips = st.number_input("Number Of Trips", min_value=1, max_value=50, value=1)
+passport = st.selectbox("Passport", ["0", "1"])
+pitchSatisfactionScore = st.number_input("Pitch Satisfaction Score", min_value=1, max_value=5, value=1)
+ownCar = st.selectbox("Own Car", ["0", "1"])
+numberOfChildrenVisiting = st.number_input("Number Of Children Visiting",
+                                            min_value=0, max_value=5, value=0)
+designation = st.selectbox("Designation", ["Executive", "Senior Manager", 'Manager', 'AVP', 'VP'])
+monthlyIncome = st.number_input("Monthly Income",
+                                min_value=0, max_value=100000, value=0)
+
+# 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
+
+}])
+
+
+if st.button("Predict Tourism Package Taken"):
+    prediction = model.predict(input_data)[0]
+    result = "Package Taken" if prediction == 1 else "Package Not Taken"
+    st.subheader("Prediction Result:")
+    st.success(f"The model predicts: **{result}**")

requirements.txt

@@ -1,3 +1,6 @@
-altair
-pandas
-streamlit
+huggingface_hub==0.32.6
+datasets==3.6.0
+pandas==2.2.2
+scikit-learn==1.6.0
+xgboost==2.1.4
+mlflow==3.0.1