Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,28 @@
-FROM python:3.13.5-slim
+# deployment/Dockerfile
 
-WORKDIR /app
+# 1. Base image
+FROM python:3.11-slim
+
+# 2. Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    HF_TOKEN=${HF_TOKEN} \
+    STREAMLIT_SERVER_HEADLESS=true \
+    STREAMLIT_SERVER_PORT=8501 \
+    STREAMLIT_SERVER_ADDRESS=0.0.0.0
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+# 3. Create working dir
+WORKDIR /app
 
-COPY requirements.txt ./
-COPY src/ ./src/
+# 4. Copy & install dependencies
+COPY requirements.txt .
+RUN pip install --upgrade pip && \
+    pip install -r requirements.txt
 
-RUN pip3 install -r requirements.txt
+# 5. Copy app code
+COPY app.py hf_deploy.py ./
 
+# 6. Expose Streamlit port
 EXPOSE 8501
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# 7. Start Streamlit
+ENTRYPOINT ["streamlit", "run", "app.py"]

app.py

@@ -0,0 +1,117 @@
+import os
+import streamlit as st
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+# -------------------------------------------------
+# Download and load the trained model from HF Model Hub
+# -------------------------------------------------
+HF_MODEL_REPO   = "Amittripipathi/Wellness_tourism_project-model"
+MODEL_FILENAME  = "best_model.joblib"
+
+model_path = hf_hub_download(
+    repo_id   = HF_MODEL_REPO,
+    repo_type = "model",
+    filename  = MODEL_FILENAME,
+    token     = os.getenv("HF_TOKEN")
+)
+model = joblib.load(model_path)
+
+# -------------------------------------------------
+# Streamlit App Configuration
+# -------------------------------------------------
+st.set_page_config(
+    page_title="Wellness Tourism Package Purchase Predictor",
+    layout="centered"
+)
+
+st.title("Wellness Tourism Package Purchase Predictor")
+st.markdown("""
+Predict whether a customer will purchase our newly launched **Wellness Tourism Package**  
+based on demographic and interaction details. Fill in the fields below and click **Predict**.
+""")
+
+# -------------------------------------------------
+# Customer Demographics Inputs
+# -------------------------------------------------
+age = st.number_input("Age", min_value=18, max_value=100, value=30)
+gender = st.selectbox("Gender", ["Male", "Female"])
+marital_status = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
+occupation = st.selectbox(
+    "Occupation",
+    ["Salaried", "Freelancer", "Business", "Student", "Retired", "Other"]
+)
+designation = st.text_input("Designation", value="")
+
+city_tier = st.selectbox("City Tier", [1, 2, 3])
+monthly_income = st.number_input(
+    "Monthly Income (Gross)", min_value=0, step=1000, value=50000
+)
+passport = st.selectbox("Valid Passport?", ["Yes", "No"])
+own_car = st.selectbox("Owns a Car?", ["Yes", "No"])
+number_of_persons = st.number_input(
+    "Number of People Visiting", min_value=1, max_value=10, value=2
+)
+number_of_children = st.number_input(
+    "Number of Children Under 5 Visiting", min_value=0, max_value=5, value=0
+)
+
+# -------------------------------------------------
+# Customer Interaction Inputs
+# -------------------------------------------------
+type_of_contact = st.selectbox(
+    "Type of Contact", ["Company Invited", "Self Inquiry"]
+)
+product_pitched = st.selectbox(
+    "Product Pitched",
+    ["Wellness Package", "Adventure Package", "Cultural Package", "Other"]
+)
+pitch_satisfaction = st.slider(
+    "Pitch Satisfaction Score", min_value=0, max_value=10, value=7
+)
+number_of_followups = st.number_input(
+    "Number of Follow-ups", min_value=0, max_value=20, value=1
+)
+duration_of_pitch = st.number_input(
+    "Duration of Pitch (minutes)", min_value=1, max_value=120, value=10
+)
+number_of_trips = st.number_input(
+    "Average Number of Trips per Year", min_value=0, max_value=50, value=2
+)
+preferred_property_star = st.number_input(
+    "Preferred Hotel Star Rating", min_value=1, max_value=5, value=3
+)
+
+# -------------------------------------------------
+# Assemble Input DataFrame
+# -------------------------------------------------
+input_df = pd.DataFrame([{
+    "Age": age,
+    "Gender": gender,
+    "MaritalStatus": marital_status,
+    "Occupation": occupation,
+    "Designation": designation,
+    "CityTier": city_tier,
+    "MonthlyIncome": monthly_income,
+    "Passport": 1 if passport == "Yes" else 0,
+    "OwnCar": 1 if own_car == "Yes" else 0,
+    "NumberOfPersonVisiting": number_of_persons,
+    "NumberOfChildrenVisiting": number_of_children,
+    "TypeofContact": type_of_contact,
+    "ProductPitched": product_pitched,
+    "PitchSatisfactionScore": pitch_satisfaction,
+    "NumberOfFollowups": number_of_followups,
+    "DurationOfPitch": duration_of_pitch,
+    "NumberOfTrips": number_of_trips,
+    "PreferredPropertyStar": preferred_property_star
+}])
+
+# -------------------------------------------------
+# Prediction & Display
+# -------------------------------------------------
+if st.button("Predict Purchase"):
+    pred = model.predict(input_df)[0]
+    label = "🚀 Will Purchase" if pred == 1 else "❌ Will Not Purchase"
+    st.subheader("Prediction Result")
+    st.success(label)

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