Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,24 @@
-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 all files from the current directory on the host to the container's /app directory
+COPY . .
 
-COPY requirements.txt ./
-COPY src/ ./src/
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
 
+# Install Python dependencies listed in requirements.txt
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
+RUN useradd -m -u 1000 user
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+WORKDIR $HOME/app
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+COPY --chown=user . $HOME/app
+
+# 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
+
+
+# Download and load the model
+model_path = hf_hub_download(repo_id="Pushpak21/tourism-package-model", filename="best_tourism_package_model.joblib")
+model = joblib.load(model_path)
+
+# Feature descriptions
+feature_info = {
+    "Age": "Age of the customer (years).",
+    "TypeofContact": "How the customer was contacted (Company Invited / Self Inquiry).",
+    "CityTier": "City category (1=Tier1, 2=Tier2, 3=Tier3).",
+    "Occupation": "Customer occupation (Salaried, Freelancer, etc.).",
+    "Gender": "Male or Female.",
+    "NumberOfPersonVisiting": "Total number of people visiting together.",
+    "PreferredPropertyStar": "Preferred hotel star rating (3,4,5).",
+    "MaritalStatus": "Single / Married / Divorced.",
+    "NumberOfTrips": "Average trips per year.",
+    "Passport": "Has passport? (0 = No, 1 = Yes).",
+    "OwnCar": "Owns car? (0 = No, 1 = Yes).",
+    "NumberOfChildrenVisiting": "Children under 5 accompanying.",
+    "Designation": "Job designation/title.",
+    "MonthlyIncome": "Gross monthly income.",
+    "PitchSatisfactionScore": "Satisfaction score for the sales pitch (1-5).",
+    "ProductPitched": "Product variant pitched to the customer.",
+    "NumberOfFollowups": "Number of follow-ups by salesperson.",
+    "DurationOfPitch": "Duration of pitch in minutes."
+}
+
+st.sidebar.title("Feature descriptions")
+for k, v in feature_info.items():
+    st.sidebar.write(f"**{k}** — {v}")
+
+# Example form using help text (tooltips)
+with st.form("input_form"):
+    age = st.number_input("Age", min_value=18, max_value=100, value=30, help=feature_info["Age"])
+    typeof_contact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"], help=feature_info["TypeofContact"])
+    city_tier = st.selectbox("City Tier", [1,2,3], help=feature_info["CityTier"])
+    occupation = st.selectbox("Occupation", ["Salaried", "Free Lancer", "Small Business", "Large Business"], help=feature_info["Occupation"])
+    gender = st.selectbox("Gender", ["Male", "Female"], help=feature_info["Gender"])
+    persons = st.number_input("Number Of Person Visiting", min_value=1, max_value=5, value=2, help=feature_info["NumberOfPersonVisiting"])
+    star = st.selectbox("Preferred Property Star", [3,4,5], help=feature_info["PreferredPropertyStar"])
+    marital = st.selectbox("Marital Status", ["Single", "Married", "Divorced","Unmarried"], help=feature_info["MaritalStatus"])
+    trips = st.number_input("Number Of Trips", min_value=1, max_value=25, value=2, help=feature_info["NumberOfTrips"])
+    passport = st.radio("Passport", [0,1], help=feature_info["Passport"])
+    owncar = st.radio("Own Car", [0,1], help=feature_info["OwnCar"])
+    children = st.number_input("Number Of Children Visiting", min_value=0, max_value=3, value=0, help=feature_info["NumberOfChildrenVisiting"])
+    designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP","VP"], help=feature_info["Designation"])
+    income = st.number_input("Monthly Income", min_value=1000, max_value=100000, value=30000, help=feature_info["MonthlyIncome"])
+    satisfaction = st.slider("Pitch Satisfaction Score", min_value=1, max_value=5, value=3, help=feature_info["PitchSatisfactionScore"])
+    product = st.selectbox("Product Pitched", ["Basic", "Standard","King", "Deluxe", "Super Deluxe"], help=feature_info["ProductPitched"])
+    followups = st.number_input("Number Of Followups", min_value=1, max_value=6, value=2, help=feature_info["NumberOfFollowups"])
+    duration = st.number_input("Duration Of Pitch (minutes)", min_value=0, max_value=300, value=10, help=feature_info["DurationOfPitch"])
+
+    submitted = st.form_submit_button("Predict")
+
+if submitted:
+    input_df = pd.DataFrame([{
+        "Age": age,
+        "TypeofContact": typeof_contact,
+        "CityTier": city_tier,
+        "Occupation": occupation,
+        "Gender": gender,
+        "NumberOfPersonVisiting": persons,
+        "PreferredPropertyStar": star,
+        "MaritalStatus": marital,
+        "NumberOfTrips": trips,
+        "Passport": passport,
+        "OwnCar": owncar,
+        "NumberOfChildrenVisiting": children,
+        "Designation": designation,
+        "MonthlyIncome": income,
+        "PitchSatisfactionScore": satisfaction,
+        "ProductPitched": product,
+        "NumberOfFollowups": followups,
+        "DurationOfPitch": duration
+    }])
+    proba = model.predict_proba(input_df)[0,1]
+    pred = model.predict(input_df)[0]
+    st.write("Probability:", round(proba,3))
+    st.write("Prediction:", "Will buy (1)" if pred==1 else "Will not buy (0)")

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