Update app.py

app.py

@@ -18,13 +18,12 @@ def _load_predictor() -> MultiModalPredictor:
     EXTRACT_DIR.mkdir(parents=True, exist_ok=True)
     with zipfile.ZipFile(ZIP_FILENAME, "r") as zf:
         zf.extractall(str(EXTRACT_DIR))
-    # If the zip contains a single folder, use it; else use EXTRACT_DIR
     contents = list(EXTRACT_DIR.iterdir())
     predictor_root = contents[0] if (len(contents) == 1 and contents[0].is_dir()) else EXTRACT_DIR
-    return MultiModalPredictor.load(str(predictor_root), require_py_version_match=False)
+    return MultiModalPredictor.load(str(predictor_root))  # ← fixed
 
 PREDICTOR = _load_predictor()
-CLASS_LABELS = {i: chr(65+i) for i in range(26)}  # 0->A ... 25->Z
+CLASS_LABELS = {i: chr(65 + i) for i in range(26)}  # 0->A ... 25->Z
 
 def predict(pil_img: Image.Image):
     if pil_img is None:
@@ -32,10 +31,13 @@ def predict(pil_img: Image.Image):
     if pil_img.mode != "RGB":
         pil_img = pil_img.convert("RGB")
     processed = pil_img.resize((224, 224))
-    tmp = pathlib.Path(tempfile.mkdtemp())
-    img_path = tmp / "input.png"
+    tmpdir = pathlib.Path(tempfile.mkdtemp())
+    img_path = tmpdir / "input.png"
     processed.save(img_path)
-    proba_df = PREDICTOR.predict_proba(pd.DataFrame({"image": [str(img_path)]}))
+
+    df = pd.DataFrame({"image": [str(img_path)]})
+    proba_df = PREDICTOR.predict_proba(df)
+
     pretty = {}
     for col in proba_df.columns:
         label = f"Letter {CLASS_LABELS[col]}" if isinstance(col, int) and 0 <= col < 26 else str(col)
@@ -49,7 +51,7 @@ EXAMPLES = [
     ["https://www.signingsavvy.com/images/words/alphabet/2/c1.jpg"],
 ]
 
-with gr.Blocks() as demo:
+with gr.Blocks(title="Sign Language Recognition") as demo:
     gr.Markdown("# Sign Language Recognition (AutoGluon Image)")
     with gr.Row():
         with gr.Column():