Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +1,23 @@
+# 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
+
+# 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
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+# 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 @@
+%%writefile tourism_project/deployment/app.py
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+ 
+model_path = hf_hub_download(repo_id="divshiva1988/Tourism_package_acceptance_predictor_model", filename="best_Tourism_package_acceptance_predictor_model_v1.joblib")
+model = joblib.load(model_path)
+
+#model=joblib.load("https://huggingface.co/divshiva1988/Tourism_package_acceptance_predictor_model/resolve/main/best_Tourism_package_acceptance_predictor_model_v1.joblib")
+
+# Streamlit UI
+st.title(" Tourism package acceptance Prediction")
+st.write("""
+This application predicts the  acceptance of a Tourism package.
+""")
+
+# User input
+Designation = st.selectbox("Designation", ["AVP", "Executive", "Manager", "SENSenior Manager", "VP"])
+Gender = st.selectbox("Gender", ["Male", "Female"])
+MaritalStatus = st.selectbox("MaritalStatus", ["Divorced", "Married", "Single", "UNmarried"])
+ProductPitched = st.selectbox("ProductPitched", ["Basic", "Deluxe", "King","Standard","Super Deluxe"])
+PreferredPropertyStar = st.selectbox("PreferredPropertyStar", [3,4,5])
+Passport = st.selectbox("Passport", [0,1])
+PitchSatisfactionScore = st.selectbox("PitchSatisfactionScore", [1,2,3,4,5])
+OwnCar = st.selectbox("OwnCar", [0,1])
+NumberOfChildrenVisiting = st.selectbox("NumberOfChildrenVisiting", [0,1,2,3])
+Occupation = st.selectbox("Occupation", ["Free Lancer","Large Business", "Salaried", "Small Business"])
+TypeofContact = st.selectbox("TypeofContact", ["Company Invited", "Self Enquiry"])
+
+Age = st.number_input("Age of person", min_value=18.0, max_value=61.0, value=16.0, step=1)
+CityTier = st.number_input("CityTier", min_value=1, max_value=3, value=1, step=1)
+DurationOfPitch = st.number_input("DurationOfPitch", min_value=5, max_value=127, value=5, step=5)
+NumberOfFollowups = st.number_input("NumberOfFollowups", min_value=1, max_value=6, value=1)
+NumberOfPersonVisiting = st.number_input("NumberOfPersonVisiting", min_value=1, max_value=5, value=5, step=1)
+NumberOfTrips = st.number_input("NumberOfTrips", min_value=1, max_value=22, value=1)
+MonthlyIncome = st.number_input("MonthlyIncome", min_value=1000,max_value=99000, value=1000,step=1000)
+
+# Assemble input into DataFrame
+input_data = pd.DataFrame([{
+    'Designation': Designation,
+    'Gender': Gender,
+    'MaritalStatus': MaritalStatus,
+    'ProductPitched': ProductPitched,
+    'PreferredPropertyStar': PreferredPropertyStar,
+    'Passport': Passport,
+    'PitchSatisfactionScore': PitchSatisfactionScore,
+    'OwnCar': OwnCar,
+    'NumberOfChildrenVisiting': NumberOfChildrenVisiting,
+    'Occupation': Occupation,
+    'TypeofContact': TypeofContact,
+    'Age': Age,
+    'CityTier': CityTier,
+    'DurationOfPitch': DurationOfPitch,
+    'NumberOfFollowups': NumberOfFollowups,
+    'NumberOfPersonVisiting': NumberOfPersonVisiting,
+    'PreferredPropertyStar': PreferredPropertyStar,
+    'NumberOfTrips': NumberOfTrips,
+    'MonthlyIncome': MonthlyIncome
+    
+}])
+
+# Predict button
+if st.button("Predict Tourism package acceptance"):
+    prediction = model.predict(input_data)[0]
+    st.subheader("Prediction Result:")
+    st.success(f"Tourism package has strong chances of getting : **${prediction:,.2f} **")

requirements.txt

@@ -0,0 +1,7 @@
+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