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 the model from the Model Hub
+model_path = hf_hub_download(repo_id="SilviaMartin/Visitwithus", filename="visit_with_us_model_v1.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+
+# Streamlit UI for Customer Churn Prediction
+st.title("Customer Purchase Prediction App")
+st.write("The Customer Purchase Prediction App predicts whether customers will purchase the newly introduced Wellness Tourism Package before contacting them")
+st.write("Kindly enter the customer details to check whether they are likely to purchase.")
+
+# Collect user input
+Age = st.number_input("Customer Age", min_value=18, max_value=61, value=40)
+CityTier = st.number_input("The city tier of customer ", min_value=1, max_value=3, value=2)
+DurationOfPitch = st.number_input("The duration of the pitch",min_value=5,max_value=127 value=20)
+NumberOfPersonVisiting = st.number_input("Number of persons visiting", min_value=1, max_value=5, value=2)
+NumberOfFollowups = st.number_input("Number of followups", min_value=1, max_value=10, value=2)
+PreferredPropertyStar = st.number_input("The preferred property star rating", min_value=3, max_value=5, value=3)
+NumberOfTrips = st.number_input("Number of trips", min_value=1, max_value=22, value=2)
+Passport = st.number_input("Having Passport?", [0, 1])
+OwnCar = st.number_input("Having Car?", [0, 1])
+NumberOfChildrenVisiting = st.number_input("Number of children visiting", min_value=0, max_value=3, value=2)
+MonthlyIncome=st.number_input("Specify the monthly income",min_value=1000,max_value=100000,value=3000)
+PitchSatisfactionScore = st.number_input("Pitch Satisfaction Score", min_value=1, max_value=5, value=3)
+TypeofContact=st.selectbox("Mention the type of contact", ["Company Invited","Self Enquiry"])
+Occupation = st.selectbox("Occupation", ["Free Lancer","Large Business","Salaried","Small Business"])
+Gender  = st.selectbox("Customer Gender", ["Female","Male"])
+ProductPitched=st.selectbox("Mention the product pitched to customer", ["Basic","Deluxe","Standard","King","Super Deluxe"])
+MaritialStatus=st.selectbox("Mention the marital status of customer", ["Single","Married","Unmarried","Divorced"])
+Designation=st.selectbox("Mention the designation of customer", ["Executive","Manager","Senior Manager","VP","AVP"])
+
+
+# Convert categorical inputs to match model training
+input_data = pd.DataFrame([{
+    'Age': Age,
+    'CityTier': CityTier,
+    'DurationOfPitch': DurationOfPitch,
+    'NumberOfPersonVisiting': NumberOfPersonVisiting,
+    'NumberOfFollowups': NumberOfFollowups,
+    'PreferredPropertyStar': PreferredPropertyStar,
+    'NumberOfTrips':NumberOfTrips,
+    'Passport':Passport,
+    'OwnCar':OwnCar,
+    'NumberOfChildrenVisiting':NumberOfChildrenVisiting,
+    'MonthlyIncome':MonthlyIncome,
+    'PitchSatisfactionScore':PitchSatisfactionScore,
+    'TypeofContact': TypeofContact,
+    'Occupation': Occupation,
+    'Gender': Gender,
+    'ProductPitched': ProductPitched,
+    'MaritalStatus': MaritialStatus,
+    '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 = "Product Purchased" if prediction == 1 else "Not Purchased"
+    st.write(f"Based on the information provided, the customer is likely to {result}.")

requirements.txt

@@ -1,3 +1,5 @@
-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