Auto-deploy from GitHub Actions

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 +1,65 @@
+
+import streamlit as st
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+# Load the trained model from Hugging Face Hub
+model_path = hf_hub_download(repo_id="maddykan101/visit_with_us_model", filename="best_model.joblib")
+model = joblib.load(model_path)
+
+st.title("Travel Package Prediction App")
+
+# Input form
+with st.form("prediction_form"):
+    Age = st.number_input("Age", min_value=18, max_value=100, value=30)
+    TypeofContact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
+    CityTier = st.selectbox("City Tier", [1, 2, 3])
+    DurationOfPitch = st.number_input("Duration Of Pitch", min_value=0, max_value=50, value=10)
+    Occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Student", "Free Lancer"])
+    Gender = st.selectbox("Gender", ["Male", "Female"])
+    NumberOfPersonVisiting = st.number_input("Number Of Persons Visiting", min_value=1, max_value=10, value=1)
+    NumberOfFollowups = st.number_input("Number Of Follow-ups", min_value=0, max_value=20, value=1)
+    ProductPitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe"])
+    PreferredPropertyStar = st.selectbox("Preferred Property Star", [1, 2, 3, 4, 5])
+    MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
+    NumberOfTrips = st.number_input("Number Of Trips", min_value=0, max_value=100, value=1)
+    Passport = st.selectbox("Passport", [0, 1])
+    PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", 1, 5, 3)
+    OwnCar = st.selectbox("Own Car", [0, 1])
+    NumberOfChildrenVisiting = st.number_input("Number Of Children Visiting", min_value=0, max_value=10, value=0)
+    Designation = st.selectbox("Designation", ["Manager", "Senior Manager", "Executive", "AVP"])
+    MonthlyIncome = st.number_input("Monthly Income", min_value=1000, max_value=100000, value=25000)
+
+    submit = st.form_submit_button("Predict")
+
+if submit:
+    # Convert inputs to DataFrame
+    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
+    }])
+
+    # Predict
+    prediction = model.predict(input_data)[0]
+
+    if prediction == 1:
+        st.success("✅ Customer is likely to purchase the product.")
+    else:
+        st.error("❌ Customer is not likely to purchase the product.")

requirements.txt

@@ -1,3 +1,9 @@
-altair
+streamlit>=1.33
 pandas
-streamlit
+scikit-learn
+torch
+transformers
+datasets
+mlflow
+joblib
+huggingface_hub>=0.23