Update app.py

app.py

@@ -13,15 +13,24 @@ import pandas as pd
 from PIL import Image, ImageDraw, ImageFont
 
 from fastai.learner import load_learner
+from fastai.vision.core import PILImage
 
-# -----------------------
-# Load model once
-# -----------------------
+# Load model
 learn = load_learner("model.pkl")
 
 
+def _file_to_path(f):
+    """
+    Gradio with type='filepath' usually returns a string path.
+    This helper safely converts any incoming file object to a path string.
+    """
+    if isinstance(f, str):
+        return f
+    return getattr(f, "name", str(f))
+
+
 def _safe_filename(name: str) -> str:
-    return "".join(c if c.isalnum() or c in ("-", "_", ".", " ") else "_" for c in name)
+    return "".join(c if c.isalnum() or c in ("-", "_", ".", " ") else "_" for c in name).strip()
 
 
 def _annotate_image(pil_img: Image.Image, text: str) -> Image.Image:
@@ -68,17 +77,18 @@ def predict_batch(files, display_mode):
     os.makedirs(ann_dir, exist_ok=True)
 
     for f in files:
-        path = str(f)
-        fname = os.path.basename(path)
+        path = _file_to_path(f)
+        fname = os.path.basename(str(path))
         safe_fname = _safe_filename(fname)
 
         try:
-            # simplest possible inference path
+            img = PILImage.create(path)
+
             with torch.inference_mode():
-                pred, _, _ = learn.predict(path)
+                with learn.no_bar(), learn.no_logging():
+                    pred, _, _ = learn.predict(img)
 
             pred_str = str(pred)
-
             pil_img = Image.open(path).convert("RGB")
 
             if display_mode == "image":
@@ -106,7 +116,7 @@ def predict_batch(files, display_mode):
             ann_img.save(out_path, quality=92)
 
         except Exception as e:
-            print(f"ERROR processing {fname}")
+            print(f"ERROR processing file: {fname}")
             traceback.print_exc()
 
             rows.append({