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src/app.py

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+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)

src/requirements.txt

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+streamlit==1.37.0
+opencv-python-headless==4.10.0.84
+numpy==1.26.4
+scikit-image==0.24.0
+scikit-learn==1.4.2
+joblib==1.4.2
+Pillow==10.3.0
+huggingface_hub==0.18.1