Update app.py

app.py

@@ -1,42 +1,26 @@
 import gradio as gr
-from PIL import Image
-from mineru_vl_utils import MinerUClient
-from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+import logging
+logging.basicConfig(level=logging.DEBUG)
 
-# Charger le modèle MinerU
-model_path = "opendatalab/MinerU2.5-2509-1.2B"
-model = Qwen2VLForConditionalGeneration.from_pretrained(
-    model_path,
-    torch_dtype="auto",
-    device_map="auto"
-)
-processor = AutoProcessor.from_pretrained(model_path, use_fast=True)
+try:
+    from mineru_vl_utils import MinerUClient
+    print("✅ mineru-vl-utils importé")
+except Exception as e:
+    print("❌ Erreur import mineru-vl-utils:", e)
 
-client = MinerUClient(
-    backend="transformers",
-    model=model,
-    processor=processor
-)
+try:
+    client = MinerUClient(
+        backend="transformers",
+        model_path="opendatalab/MinerU2.5-2509-1.2B"
+    )
+    print("✅ Client MinerU initialisé")
+except Exception as e:
+    print("❌ Erreur init MinerUClient:", e)
 
-def extract_from_image(image):
-    # Conversion si nécessaire
-    if not isinstance(image, Image.Image):
-        image = Image.fromarray(image)
+def test_fn():
+    return "Space lancé avec succès ✅"
 
-    # Extraction
-    blocks = client.two_step_extract(image)
-    # On retourne le texte concaténé
-    extracted_text = "\n".join([b.text for b in blocks if hasattr(b, "text")])
-    return extracted_text
-
-# Interface Gradio
-demo = gr.Interface(
-    fn=extract_from_image,
-    inputs=gr.Image(type="pil"),
-    outputs="text",
-    title="MinerU2.5 - Document Extract",
-    description="Upload an image or PDF page and extract structured text with MinerU2.5"
-)
+demo = gr.Interface(fn=test_fn, inputs=[], outputs="text")
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()