Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,54 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 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]
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+        st.success(f"Prediksi: **{CLASS_NAMES[pred]}**")
+        
+        st.write("Confidence:")
+        st.bar_chart(prob)