fixed and run app.py

app.py

@@ -1,41 +1,97 @@
-import os
-import sys
-import subprocess
+import torch
+from transformers import AutoModelForVision2Seq, AutoProcessor, BitsAndBytesConfig
+from PIL import Image
+import gradio as gr
 
-def install_packages():
-    print("Installing packages...")
-    subprocess.check_call([sys.executable, '-m', 'pip', 'install', '--upgrade', 'pip'])
-    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'huggingface_hub'])
-    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'transformers'])
-    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'torch', '--index-url', 'https://download.pytorch.org/whl/cpu'])
-    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'gradio'])
-    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'Pillow'])
-    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'bitsandbytes'])
-    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'accelerate'])
+# Global variables for model and processor
+model = None
+processor = None
 
-if __name__ == "__main__":
+def load_model_and_processor():
+    global model, processor
     try:
-        install_packages()
-        print("Package installation completed")
-        
-        import gradio as gr
-        import torch
-        from transformers import AutoProcessor
-        
-        def process_handwriting(image):
-            if image is None:
-                return "กรุณาอัพโหลดรูปภาพ"
-            return f"ทดสอบระบบ: Torch version: {torch.__version__}, Transformers installed"
-        
-        demo = gr.Interface(
-            fn=process_handwriting,
-            inputs=gr.Image(type="pil", label="อัพโหลดรูปภาพ"),
-            outputs=gr.Textbox(label="ผลลัพธ์"),
-            title="Test Installation",
-            description="ทดสอบการติดตั้ง libraries"
+        model_path = "Aekanun/thai-handwriting-llm"
+        base_model_path = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+        
+        print("Loading processor...")
+        processor = AutoProcessor.from_pretrained(base_model_path)
+        
+        print("Loading model...")
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=torch.bfloat16
         )
         
-        demo.launch()
+        model = AutoModelForVision2Seq.from_pretrained(
+            model_path,
+            device_map="auto",
+            torch_dtype=torch.bfloat16,
+            quantization_config=bnb_config
+        )
+        return True
+    except Exception as e:
+        print(f"Error loading model: {str(e)}")
+        return False
+
+def process_handwriting(image):
+    global model, processor
+    
+    if image is None:
+        return "กรุณาอัพโหลดรูปภาพ"
+    
+    try:
+        if not isinstance(image, Image.Image):
+            image = Image.fromarray(image)
+        
+        prompt = """Transcribe the Thai handwritten text from the provided image.
+Only return the transcription in Thai language."""
+        
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": prompt},
+                    {"type": "image", "image": image}
+                ],
+            }
+        ]
+        
+        text = processor.apply_chat_template(messages, tokenize=False)
+        inputs = processor(text=text, images=image, return_tensors="pt")
+        inputs = {k: v.to(model.device) for k, v in inputs.items()}
+        
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=256,
+                do_sample=False,
+                pad_token_id=processor.tokenizer.pad_token_id
+            )
+        
+        transcription = processor.decode(outputs[0], skip_special_tokens=True)
+        return transcription
+        
     except Exception as e:
-        print(f"Error occurred: {str(e)}")
-        raise e
+        return f"เกิดข้อผิดพลาด: {str(e)}"
+
+# Initialize application
+print("Initializing application...")
+model_loaded = load_model_and_processor()
+
+if model_loaded:
+    print("Creating Gradio interface...")
+    demo = gr.Interface(
+        fn=process_handwriting,
+        inputs=gr.Image(type="pil", label="อัพโหลดรูปลายมือเขียนภาษาไทย"),
+        outputs=gr.Textbox(label="ข้อความที่แปลงได้"),
+        title="Thai Handwriting to Text",
+        description="อัพโหลดรูปภาพลายมือเขียนภาษาไทยเพื่อแปลงเป็นข้อความ"
+    )
+    
+    if __name__ == "__main__":
+        print("Launching application...")
+        demo.launch()
+else:
+    print("Failed to load model and processor. Please check the logs.")

app.py.origi

@@ -0,0 +1,41 @@
+import os
+import sys
+import subprocess
+
+def install_packages():
+    print("Installing packages...")
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', '--upgrade', 'pip'])
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'huggingface_hub'])
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'transformers'])
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'torch', '--index-url', 'https://download.pytorch.org/whl/cpu'])
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'gradio'])
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'Pillow'])
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'bitsandbytes'])
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'accelerate'])
+
+if __name__ == "__main__":
+    try:
+        install_packages()
+        print("Package installation completed")
+        
+        import gradio as gr
+        import torch
+        from transformers import AutoProcessor
+        
+        def process_handwriting(image):
+            if image is None:
+                return "กรุณาอัพโหลดรูปภาพ"
+            return f"ทดสอบระบบ: Torch version: {torch.__version__}, Transformers installed"
+        
+        demo = gr.Interface(
+            fn=process_handwriting,
+            inputs=gr.Image(type="pil", label="อัพโหลดรูปภาพ"),
+            outputs=gr.Textbox(label="ผลลัพธ์"),
+            title="Test Installation",
+            description="ทดสอบการติดตั้ง libraries"
+        )
+        
+        demo.launch()
+    except Exception as e:
+        print(f"Error occurred: {str(e)}")
+        raise e