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 +1,84 @@
+import streamlit as st
+import pandas as pd
+import joblib
+import os
+from huggingface_hub import hf_hub_download
+
+# 1. Configuration - Matching your train.py setup
+REPO_ID = "SagarAtHf/tourismpackagepredict-model"
+FILENAME = "productionmodel.joblib"
+
+@st.cache_resource # This ensures the model only downloads once, not on every click
+def load_model():
+    try:
+        # Pulling the model from the Model Hub
+        model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+        return joblib.load(model_path)
+    except Exception as e:
+        st.error(f"Error loading model from Hub: {e}")
+        return None
+
+# Load the model
+model = load_model()
+
+# 2. UI Header
+st.title("🌴 Wellness Tourism Package Predictor")
+st.markdown("Enter customer details below to predict the likelihood of a package purchase.")
+
+# 3. User Input Form
+with st.form("prediction_form"):
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        age = st.number_input("Age", min_value=18, max_value=100, value=30)
+        city_tier = st.selectbox("City Tier", [1, 2, 3])
+        occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free Lancer"])
+        gender = st.selectbox("Gender", ["Male", "Female"])
+        duration = st.number_input("Duration of Pitch", value=15)
+        
+    with col2:
+        marital_status = st.selectbox("Marital Status", ["Married", "Unmarried", "Divorced"])
+        designation = st.selectbox("Designation", ["Manager", "Executive", "Senior Manager", "AVP", "VP"])
+        product_pitched = st.selectbox("Product Pitched", ["Deluxe", "Basic", "Standard", "Super Deluxe", "King"])
+        monthly_income = st.number_input("Monthly Income", value=20000)
+        passport = st.selectbox("Has Passport?", [0, 1], format_func=lambda x: "Yes" if x==1 else "No")
+
+    # Additional features required by the model (using averages/defaults)
+    submit = st.form_submit_button("Predict Probability")
+
+# 4. Prediction Logic
+if submit and model:
+    # Prepare input dataframe with exact column names from training
+    input_data = pd.DataFrame({
+        'Age': [age],
+        'CityTier': [city_tier],
+        'DurationOfPitch': [duration],
+        'Occupation': [occupation],
+        'Gender': [gender],
+        'NumberOfPersonVisiting': [2], # Defaulting common values
+        'NumberOfFollowups': [3],
+        'ProductPitched': [product_pitched],
+        'PreferredPropertyStar': [3],
+        'MaritalStatus': [marital_status],
+        'NumberOfTrips': [1],
+        'Passport': [passport],
+        'PitchSatisfactionScore': [3],
+        'OwnCar': [1],
+        'NumberOfChildrenVisiting': [0],
+        'Designation': [designation],
+        'MonthlyIncome': [monthly_income],
+        'TypeofContact': ["Self Enquiry"]
+    })
+
+    # Get probability from the model
+    # Note: We use the threshold 0.45 you defined in your local tests
+    prob = model.predict_proba(input_data)[0][1]
+    prediction = 1 if prob >= 0.45 else 0
+
+    # 5. Display Results
+    st.divider()
+    if prediction == 1:
+        st.success(f"🎯 High Potential Customer! (Probability: {prob:.2%})")
+        st.balloons()
+    else:
+        st.warning(f"⏳ Low Likelihood of purchase. (Probability: {prob:.2%})")

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