Upload folder using huggingface_hub

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install system deps (minimal)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+ && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py .
+
+EXPOSE 7860
+
+CMD ["streamlit", "run", "app.py", "--server.port", "7860", "--server.address", "0.0.0.0"]

app.py

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+
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+
+from huggingface_hub import hf_hub_download
+from datasets import load_dataset
+
+# -----------------------------
+# CONFIG (edit if needed)
+# -----------------------------
+HF_MODEL_REPO = "nansri/wellness-purchase-predictor"
+MODEL_FILENAME = "best_model.joblib"
+
+# Used only to build dropdown options + default values
+HF_DATASET_REPO = "nansri/visit-with-us-wellness"
+TRAIN_FILE = "processed/train.csv"
+
+st.set_page_config(page_title="Wellness Package Predictor", layout="centered")
+
+# -----------------------------
+# Load model from HF hub
+# -----------------------------
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(repo_id=HF_MODEL_REPO, filename=MODEL_FILENAME, repo_type="model")
+    return joblib.load(model_path)
+
+# -----------------------------
+# Load metadata (optional but helpful)
+# -----------------------------
+@st.cache_data
+def load_train_metadata():
+    ds = load_dataset(HF_DATASET_REPO, data_files={"train": TRAIN_FILE})
+    train_df = ds["train"].to_pandas()
+    # feature columns (exclude target)
+    feature_cols = [c for c in train_df.columns if c != "ProdTaken"]
+    # identify numeric vs categorical
+    num_cols = train_df[feature_cols].select_dtypes(include=np.number).columns.tolist()
+    cat_cols = [c for c in feature_cols if c not in num_cols]
+
+    # defaults
+    medians = {c: float(train_df[c].median()) for c in num_cols}
+    modes = {c: str(train_df[c].mode(dropna=True).iloc[0]) if train_df[c].notna().any() else "" for c in cat_cols}
+    # categories for dropdown
+    categories = {c: sorted([str(x) for x in train_df[c].dropna().unique().tolist()]) for c in cat_cols}
+
+    return feature_cols, num_cols, cat_cols, medians, modes, categories
+
+model = load_model()
+feature_cols, num_cols, cat_cols, medians, modes, categories = load_train_metadata()
+
+# -----------------------------
+# UI
+# -----------------------------
+st.title("Wellness Tourism Package Purchase Predictor")
+st.write("Enter customer details to predict likelihood of purchasing the Wellness Tourism Package.")
+
+inputs = {}
+
+with st.form("input_form"):
+    st.subheader("Customer & Interaction Inputs")
+
+    # Numeric inputs
+    st.markdown("**Numeric Features**")
+    for col in num_cols:
+        default_val = medians.get(col, 0.0)
+        # int-like columns can still be float in data; allow float entry safely
+        inputs[col] = st.number_input(col, value=float(default_val))
+
+    # Categorical inputs
+    st.markdown("**Categorical Features**")
+    for col in cat_cols:
+        opts = categories.get(col, [])
+        default_opt = modes.get(col, opts[0] if opts else "")
+        if default_opt not in opts and opts:
+            default_opt = opts[0]
+        if opts:
+            inputs[col] = st.selectbox(col, options=opts, index=opts.index(default_opt))
+        else:
+            inputs[col] = st.text_input(col, value=default_opt)
+
+    submitted = st.form_submit_button("Predict")
+
+if submitted:
+    # Create dataframe from inputs (rubric requirement)
+    input_df = pd.DataFrame([inputs], columns=feature_cols)
+    st.write("### Input DataFrame")
+    st.dataframe(input_df)
+
+    # Predict
+    try:
+        proba = model.predict_proba(input_df)[:, 1][0]
+        pred = int(proba >= 0.5)
+        st.success(f"Prediction (ProdTaken): {pred}  |  Purchase Probability: {proba:.3f}")
+    except Exception:
+        pred = int(model.predict(input_df)[0])
+        st.success(f"Prediction (ProdTaken): {pred}")

push_to_hf_space.py

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+
+from huggingface_hub import HfApi
+import os
+
+HF_USERNAME = "nansri"  # change if needed
+SPACE_NAME = "wellness-predictor-space"
+SPACE_REPO = f"{HF_USERNAME}/{SPACE_NAME}"
+
+DEPLOY_DIR = "/content/drive/MyDrive/PGP -GL/MLOps/deployment"
+
+api = HfApi()
+
+# Create Docker Space (required since we have Dockerfile)
+api.create_repo(
+    repo_id=SPACE_REPO,
+    repo_type="space",
+    space_sdk="docker",
+    exist_ok=True
+)
+
+# Upload the entire deployment folder
+api.upload_folder(
+    folder_path=DEPLOY_DIR,
+    repo_id=SPACE_REPO,
+    repo_type="space"
+)
+
+print("✅ Deployed to HF Space:", SPACE_REPO)

requirements.txt

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+streamlit
+pandas
+numpy
+scikit-learn
+joblib
+huggingface_hub
+datasets