src/streamlit_app.py

import streamlit as st
import joblib
import numpy as np
import tempfile
import cv2
from huggingface_hub import hf_hub_download
from utils.features import extract_feature_vector

st.set_page_config(page_title="ASL Alphabet Classifier", layout="centered")

# =========================
#  LOAD MODEL FROM HF HUB
# =========================
MODEL_REPO = "bimo177x/model"
MODEL_FILE = "asl_random_forest_v1.joblib"

@st.cache_resource
def load_model():
    model_path = hf_hub_download(
        repo_id=MODEL_REPO,
        filename=MODEL_FILE
    )
    return joblib.load(model_path)

model = load_model()

CLASS_NAMES = list("ABCDEFGHIJKLMNOPQRSTUVWXYZ")

st.title("ASL Alphabet Image Classifier")
st.write("Unggah gambar tangan berpose alfabet ASL. Sistem akan memproses dan mengklasifikasinya.")

uploaded = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])

if uploaded:
    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
        tmp.write(uploaded.read())
        temp_path = tmp.name

    st.image(uploaded, caption="Uploaded Image", width=300)
    st.write("🔍 Extracting features...")

    features = extract_feature_vector(temp_path)

    if features is None:
        st.error("Tidak bisa memproses gambar.")
    else:
        feats = features.reshape(1, -1)
        pred = model.predict(feats)[0]
        prob = model.predict_proba(feats)[0]

        st.success(f"Prediksi: **{CLASS_NAMES[pred]}**")
        
        st.write("Confidence:")
        st.bar_chart(prob)