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 +1,75 @@
+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="PSstark/Machine-Learning-Prediction", filename="best_prediction_model_v1.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI for Machine Failure Prediction
+st.title("Tourism Product Purchase Prediction App")
+st.write("""
+Welcome to the **Tourism Product Purchase Prediction App**! 🌍✨
+
+This tool predicts whether a customer is likely to purchase a tourism product based on their personal details, preferences, and interaction history.
+
+Please provide the customer information below, and the model will estimate the likelihood of them taking the product.
+""")
+
+# Basic demographic info
+age = st.number_input("Customer Age", min_value=18, max_value=80, value=35)
+gender = st.selectbox("Gender", ["Male", "Female"])
+marital_status = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
+
+# Contact and occupation info
+typeof_contact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
+occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free Lancer"])
+
+# Travel and product preferences
+city_tier = st.selectbox("City Tier", [1, 2, 3])
+product_pitched = st.selectbox("Product Pitched", ["Basic", "Deluxe", "Standard", "Super Deluxe", "King"])
+designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
+
+# Numeric customer interaction details
+duration_of_pitch = st.number_input("Duration of Pitch (minutes)", min_value=0.0, max_value=100.0, value=10.0)
+number_of_person_visiting = st.number_input("Number of Persons Visiting", min_value=1, max_value=10, value=2)
+number_of_followups = st.number_input("Number of Follow-ups", min_value=0, max_value=20, value=2)
+preferred_property_star = st.selectbox("Preferred Property Star", [1, 2, 3, 4, 5])
+number_of_trips = st.number_input("Number of Trips Taken", min_value=0, max_value=50, value=5)
+pitch_satisfaction_score = st.slider("Pitch Satisfaction Score", min_value=1, max_value=5, value=3)
+
+# Additional info
+passport = st.selectbox("Passport", [0, 1])
+own_car = st.selectbox("Own Car", [0, 1,2,3])
+number_of_children_visiting = st.number_input("Number of Children Visiting", min_value=0, max_value=10, value=0)
+monthly_income = st.number_input("Monthly Income", min_value=0.0, max_value=1000000.0, value=25000.0)
+
+# 📊 Assemble all inputs into a DataFrame
+input_data = pd.DataFrame([{
+    'Age': age,
+    'TypeofContact': typeof_contact,
+    'CityTier': city_tier,
+    'DurationOfPitch': duration_of_pitch,
+    'Occupation': occupation,
+    'Gender': gender,
+    'NumberOfPersonVisiting': number_of_person_visiting,
+    'NumberOfFollowups': number_of_followups,
+    'ProductPitched': product_pitched,
+    'PreferredPropertyStar': preferred_property_star,
+    'MaritalStatus': marital_status,
+    'NumberOfTrips': number_of_trips,
+    'Passport': passport,
+    'PitchSatisfactionScore': pitch_satisfaction_score,
+    'OwnCar': own_car,
+    'NumberOfChildrenVisiting': number_of_children_visiting,
+    'Designation': designation,
+    'MonthlyIncome': monthly_income
+}])
+
+# 🔮 Make prediction
+if st.button("Predict Purchase"):
+    prediction = model.predict(input_data)[0]
+    result = "✅ Customer is Likely to Purchase the Product" if prediction == 1 else "❌ Customer is Unlikely to Purchase the Product"
+    st.subheader("Prediction Result:")
+    st.success(result)

requirements.txt

@@ -1,3 +1,7 @@
-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
+mlflow==3.0.1