Deploy Streamlit app

Dockerfile

@@ -1,20 +1,7 @@
-FROM python:3.13.5-slim
 
+FROM python:3.10-slim
 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/
-
-RUN pip3 install -r requirements.txt
-
-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"]
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

app.py

@@ -0,0 +1,33 @@
+
+import streamlit as st
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+MODEL_REPO = "Srinivas1969/Tourism-Model"
+MODEL_FILE = "rf_model.joblib"
+
+@st.cache_resource
+def load_model():
+    path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE, repo_type="model")
+    return joblib.load(path)
+
+model = load_model()
+
+st.title("Wellness Tourism Package Prediction")
+
+st.write("Enter customer details:")
+
+# NOTE: Use encoded columns
+# For simplicity, we accept numeric inputs only
+# (You can expand this to full categorical UI later)
+
+inputs = {}
+for col in ['Unnamed: 0', 'Age', 'CityTier', 'DurationOfPitch', 'NumberOfPersonVisiting', 'NumberOfFollowups', 'PreferredPropertyStar', 'NumberOfTrips', 'Passport', 'PitchSatisfactionScore', 'OwnCar', 'NumberOfChildrenVisiting', 'MonthlyIncome', 'TypeofContact_Self Enquiry', 'Occupation_Large Business', 'Occupation_Salaried', 'Occupation_Small Business', 'Gender_Female', 'Gender_Male', 'ProductPitched_Deluxe', 'ProductPitched_King', 'ProductPitched_Standard', 'ProductPitched_Super Deluxe', 'MaritalStatus_Married', 'MaritalStatus_Single', 'MaritalStatus_Unmarried', 'Designation_Executive', 'Designation_Manager', 'Designation_Senior Manager', 'Designation_VP']:
+    inputs[col] = st.number_input(col, value=0)
+
+df = pd.DataFrame([inputs])
+
+if st.button("Predict"):
+    pred = model.predict(df)[0]
+    st.success("Will Purchase" if pred==1 else "Will Not Purchase")

requirements.txt

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