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 "8500" and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8500", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -0,0 +1,69 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download the model from the Model Hub
+model_path = hf_hub_download(repo_id="rishabhsinghjk/Tourism-package-predict-model", filename="best_tour_pkg_predct_v1.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+
+# Streamlit UI for Customer Churn Prediction
+st.title("Tourism package Customer Acceptance Prediction App")
+st.write("The Tourism package Customer Acceptance Prediction App is an internal tool for tourism comapny employees to predicts whether customers will accept the pitched package based on their details.")
+st.write("Kindly enter the customer details to check whether they are likely to accept.")
+
+# Collect user input
+DurationOfPitch = st.number_input("Duration Of Pitch (Time duration in minutes)", min_value=1.0, max_value=100.0, value=1.0)
+TypeofContact = st.selectbox("Type of Contact (Method by which the customer was contacted)", ["Self Enquiry", "Company Invited"])
+Age = st.number_input("Age (customer's age in years)", min_value=18, max_value=100, value=25)
+NumberOfPersonVisiting = st.number_input("Number Of Person Visiting",min_value=1, value=2)
+NumberOfFollowups = st.number_input("Number Of Followups (Follow-ups done with the customer)", min_value=0.0, value=1.0)
+NumberOfTrips = st.number_input("Number Of Trips (Number of trips the customer takes annually.)", min_value=0.0, value=1.0)
+NumberOfChildrenVisiting = st.number_input("Number Of Children Visiting", min_value=0, value=0)
+MonthlyIncome = st.number_input("Monthly Income of customer", min_value=100.0, value=10000.0)
+CityTier = st.selectbox("City Tier of customer", ["1", "2", "3"])
+Occupation = st.selectbox("Occupation of customer", ["Salaried", "Small Business", "Large Business", "Free Lancer"])
+Gender = st.selectbox("Gender", ["Male", "Female"])
+ProductPitched = st.selectbox("Product category Pitched to customer", ["Basic", "Deluxe", "Standard", "Super Deluxe", "King"])
+PreferredPropertyStar = st.selectbox("Preferred Property Star", ["1", "2", "3", "4", "5"])
+MaritalStatus = st.selectbox("Marital Status", ["Married", "Single", "Divorced", "Unmarried"])
+Passport = st.selectbox("Has Passport?", ["Yes", "No"])
+PitchSatisfactionScore = st.selectbox("Pitch Satisfaction Score by  customer", ["5", "4", "3", "2", "1"])
+OwnCar = st.selectbox("Customer owns Car?", ["Yes", "No"])
+Designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
+
+   
+
+# Convert categorical inputs to match model training
+input_data = pd.DataFrame([{
+    'DurationOfPitch': DurationOfPitch,
+    'TypeofContact': TypeofContact,
+    'Age': Age,
+    'NumberOfPersonVisiting': NumberOfPersonVisiting,
+    'NumberOfFollowups': NumberOfFollowups,
+    'NumberOfTrips': NumberOfTrips,
+    'NumberOfChildrenVisiting': NumberOfChildrenVisiting,
+    'MonthlyIncome': MonthlyIncome,
+    'CityTier': CityTier,
+    'Occupation': Occupation,
+    'Gender': Gender,
+    'ProductPitched': ProductPitched,
+    'PreferredPropertyStar': PreferredPropertyStar,
+    'MaritalStatus': MaritalStatus,       
+    'Passport': 1 if Passport == "Yes" else 0,
+    'PitchSatisfactionScore': PitchSatisfactionScore,
+    'OwnCar': 1 if OwnCar == "Yes" else 0,
+    'Designation': Designation
+}])
+
+# 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 = "accept" if prediction == 1 else "not accept"
+    st.write(f"Based on the information provided, the customer is likely to {result} package.")

requirements.txt

@@ -1,3 +1,8 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+numpy==2.2.2
+huggingface_hub==0.32.6
+streamlit==1.43.2
+joblib==1.5.1
+scikit-learn==1.6.0
+xgboost==3.0.4
+mlflow==3.3.1