Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,27 @@
-FROM python:3.13.5-slim
+# Use a lightweight Python image
+FROM python:3.11-slim
 
-WORKDIR /app
+# Prevents Python from writing .pyc files and buffering stdout/stderr
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+# Set working directory
+WORKDIR /app
 
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy project files
+COPY . /app
 
-RUN pip3 install -r requirements.txt
+# Upgrade pip and install dependencies
+RUN pip install --upgrade pip \
+    && pip install -r tourism_project/deployment/requirements.txt
 
-EXPOSE 8501
+# Expose the port used by Streamlit
+EXPOSE 7860
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# Streamlit specific environment variables (optional tweaks)
+ENV STREAMLIT_SERVER_HEADLESS=true
+ENV STREAMLIT_SERVER_PORT=7860
+ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Default command to run the Streamlit app
+CMD ["streamlit", "run", "tourism_project/deployment/app.py", "--server.port=7860", "--server.address=0.0.0.0"]

app.py

@@ -0,0 +1,96 @@
+import streamlit as st
+import pandas as pd
+import joblib
+
+from huggingface_hub import hf_hub_download
+
+
+MODEL_REPO_ID = "bhumitps/tourism_model"
+MODEL_FILENAME = "best_tourism_model_v1.joblib"
+
+
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(
+        repo_id=MODEL_REPO_ID,
+        filename=MODEL_FILENAME,
+        repo_type="model",
+    )
+    model = joblib.load(model_path)
+    return model
+
+
+model = load_model()
+
+st.title("Wellness Tourism Package Purchase Prediction")
+
+st.write(
+    """
+    Predict whether a customer is likely to purchase the **Wellness Tourism Package**.
+
+    Fill in the customer details below and click **Predict**.
+    """
+)
+
+# --- Input fields ---
+col1, col2 = st.columns(2)
+
+with col1:
+    Age = st.number_input("Age", min_value=0, max_value=100, value=35)
+    CityTier = st.selectbox("CityTier", options=[1, 2, 3], index=0)
+    DurationOfPitch = st.number_input("DurationOfPitch (minutes)", min_value=0, max_value=300, value=15)
+    NumberOfPersonVisiting = st.number_input("NumberOfPersonVisiting", min_value=1, max_value=20, value=2)
+    NumberOfFollowups = st.number_input("NumberOfFollowups", min_value=0, max_value=20, value=2)
+    PreferredPropertyStar = st.selectbox("PreferredPropertyStar", options=[1, 2, 3, 4, 5], index=2)
+    NumberOfTrips = st.number_input("NumberOfTrips", min_value=0, max_value=50, value=1)
+    NumberOfChildrenVisiting = st.number_input("NumberOfChildrenVisiting", min_value=0, max_value=10, value=0)
+    MonthlyIncome = st.number_input("MonthlyIncome", min_value=0, max_value=1000000, value=50000, step=1000)
+
+with col2:
+    TypeofContact = st.selectbox("TypeofContact", options=["Self Enquiry", "Company Invited", "Other"])
+    Occupation = st.selectbox("Occupation", options=["Salaried", "Self Employed", "Free Lancer", "Other"])
+    Gender = st.selectbox("Gender", options=["Male", "Female", "Other"])
+    ProductPitched = st.text_input("ProductPitched (raw value)", value="Basic")
+    MaritalStatus = st.selectbox("MaritalStatus", options=["Married", "Single", "Divorced", "Other"])
+    Passport = st.selectbox("Passport", options=["No", "Yes"])
+    PitchSatisfactionScore = st.selectbox("PitchSatisfactionScore", options=[1, 2, 3, 4, 5], index=2)
+    OwnCar = st.selectbox("OwnCar", options=["No", "Yes"])
+    Designation = st.selectbox("Designation", options=["Executive", "Manager", "Senior Manager", "AVP", "VP", "Other"])
+
+st.markdown("---")
+
+if st.button("Predict"):
+    # Build a single-row DataFrame. Column names must match training features.
+    input_data = pd.DataFrame([{
+        "Age": Age,
+        "TypeofContact": TypeofContact,
+        "CityTier": CityTier,
+        "DurationOfPitch": DurationOfPitch,
+        "Occupation": Occupation,
+        "Gender": Gender,
+        "NumberOfPersonVisiting": NumberOfPersonVisiting,
+        "NumberOfFollowups": NumberOfFollowups,
+        "ProductPitched": ProductPitched,
+        "PreferredPropertyStar": PreferredPropertyStar,
+        "MaritalStatus": MaritalStatus,
+        "NumberOfTrips": NumberOfTrips,
+        "Passport": Passport,
+        "PitchSatisfactionScore": PitchSatisfactionScore,
+        "OwnCar": OwnCar,
+        "NumberOfChildrenVisiting": NumberOfChildrenVisiting,
+        "Designation": Designation,
+        "MonthlyIncome": MonthlyIncome,
+    }])
+
+    # The training pipeline (data_prep + train.py) used label encoding and scaling.
+    # This app relies on the model pipeline's own preprocessing, so we pass raw values.
+    pred_proba = model.predict_proba(input_data)[0][1]
+    pred_label = model.predict(input_data)[0]
+
+    st.subheader("Prediction Result")
+    if pred_label == 1:
+        st.success(f"Customer is **LIKELY** to purchase the Wellness Tourism Package. (Probability: {pred_proba:.2%})")
+    else:
+        st.info(f"Customer is **UNLIKELY** to purchase the Wellness Tourism Package. (Probability: {pred_proba:.2%})")
+
+    st.caption("Note: probabilities are model-based estimates and not guarantees.")

requirements.txt

@@ -1,3 +1,7 @@
-altair
 pandas
-streamlit
+numpy
+scikit-learn
+xgboost
+streamlit
+huggingface_hub
+joblib