Upload folder using huggingface_hub

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 +1,126 @@
+# tourism_project/deployment/app.py
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+import os
+
+st.set_page_config(page_title="Visa With Us - Prediction App", layout="centered")
+
+# --------------------------
+# CONFIG
+# --------------------------
+MODEL_REPO = "Dewasheesh/test-mlops"
+MODEL_FILENAME = "best_test-mlops_v1.joblib"
+
+@st.cache_resource
+def load_model(repo_id: str, filename: str):
+    """Download and load joblib model from Hugging Face Hub (cached)."""
+    try:
+        #st.info("Loading model...")
+        model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+        model = joblib.load(model_path)
+        return model
+    except Exception as e:
+        st.error(f"Failed to load model: {e}")
+        return None
+
+model = load_model(MODEL_REPO, MODEL_FILENAME)
+
+st.title("Visa With Us - Prediction App")
+st.write(
+    "This app predicts whether a customer will purchase the Wellness Tourism Package."
+)
+
+st.markdown("---")
+st.header("Features")
+
+# Numeric Inputs
+Age = st.number_input("Age", min_value=0, max_value=120, value=35)
+CityTier = st.selectbox("City Tier", [1, 2, 3], index=1)
+DurationOfPitch = st.number_input("Duration Of Pitch (minutes)", 0, 600, 10)
+NumberOfPersonVisiting = st.number_input("Number Of Persons Visiting", 1, 20, 2)
+NumberOfFollowups = st.number_input("Number Of Followups", 0, 50, 1)
+PreferredPropertyStar = st.number_input("Preferred Property Star", 1, 7, 4)
+NumberOfTrips = st.number_input("Number Of Trips (past)", 0, 50, 2)
+Passport = st.selectbox("Passport", [1, 0], index=1)
+PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", 0, 10, 7)
+OwnCar = st.selectbox("Own Car", [1, 0], index=1)
+NumberOfChildrenVisiting = st.number_input("Number Of Children Visiting", 0, 10, 0)
+MonthlyIncome = st.number_input("Monthly Income", 0, 10_000_000, 50000, step=1000)
+
+
+# --------------------------
+# CATEGORICAL VALUES
+# --------------------------
+TYPEOFCONTACT = ["Self Enquiry", "Company Invited"]
+
+OCCUPATION = ["Salaried", "Small Business", "Large Business", "Free Lancer"]
+
+GENDER = ["Male", "Female"]
+
+PRODUCTPITCHED = ["Basic", "Deluxe", "Standard", "Super Deluxe", "King"]
+
+MARITALSTATUS = ["Married", "Divorced", "Unmarried"]
+
+DESIGNATION = ["Executive", "Manager", "Senior Manager", "AVP", "VP"]
+
+# Selectboxes for categories
+TypeofContact = st.selectbox("Type of Contact", TYPEOFCONTACT)
+Occupation = st.selectbox("Occupation", OCCUPATION)
+Gender = st.selectbox("Gender", GENDER)
+ProductPitched = st.selectbox("Product Pitched", PRODUCTPITCHED)
+MaritalStatus = st.selectbox("Marital Status", MARITALSTATUS)
+Designation = st.selectbox("Designation", DESIGNATION)
+
+# Assemble input
+input_data = pd.DataFrame([{
+    "Age": Age,
+    "CityTier": CityTier,
+    "DurationOfPitch": DurationOfPitch,
+    "NumberOfPersonVisiting": NumberOfPersonVisiting,
+    "NumberOfFollowups": NumberOfFollowups,
+    "PreferredPropertyStar": PreferredPropertyStar,
+    "NumberOfTrips": NumberOfTrips,
+    "Passport": Passport,
+    "PitchSatisfactionScore": PitchSatisfactionScore,
+    "OwnCar": OwnCar,
+    "NumberOfChildrenVisiting": NumberOfChildrenVisiting,
+    "MonthlyIncome": MonthlyIncome,
+
+    "TypeofContact": TypeofContact,
+    "Occupation": Occupation,
+    "Gender": Gender,
+    "ProductPitched": ProductPitched,
+    "MaritalStatus": MaritalStatus,
+    "Designation": Designation,
+}])
+
+st.markdown("### Preview Input")
+st.dataframe(input_data)
+
+# --------------------------
+# PREDICT
+# --------------------------
+if st.button("Predict"):
+    if model is None:
+        st.error("Model not loaded.")
+    else:
+        try:
+            pred = model.predict(input_data)[0]
+
+            # probability
+            proba_text = ""
+            if hasattr(model, "predict_proba"):
+                proba = model.predict_proba(input_data)
+                if proba.shape[1] == 2:
+                    proba_text = f" (Probability: {proba[0,1]:.3f})"
+
+            result = "Purchase" if int(pred) == 1 else "No Purchase"
+            st.success(f"Prediction: **{result}**{proba_text}")
+
+        except Exception as e:
+            st.error(f"Prediction failed: {e}")
+
+st.markdown("---")
+st.caption("All categorical fields are restricted to valid training values to prevent model mismatch.")

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