fixed app.py

app.py

@@ -0,0 +1,130 @@
+import os
+import warnings
+import torch
+import gc
+from transformers import AutoModelForVision2Seq, AutoProcessor
+from peft import PeftModel
+from PIL import Image
+import gradio as gr
+from huggingface_hub import login
+
+# Basic settings
+warnings.filterwarnings('ignore')
+os.environ["CUDA_VISIBLE_DEVICES"] = ""  # ปิดการใช้ CUDA
+
+# Global variables
+model = None
+processor = None
+
+# Login to Hugging Face Hub
+if 'HUGGING_FACE_HUB_TOKEN' in os.environ:
+   print("กำลังเข้าสู่ระบบ Hugging Face Hub...")
+   login(token=os.environ['HUGGING_FACE_HUB_TOKEN'])
+else:
+   print("คำเตือน: ไม่พบ HUGGING_FACE_HUB_TOKEN")
+
+def load_model_and_processor():
+   """โหลดโมเดลและ processor"""
+   global model, processor
+   print("กำลังโหลดโมเดลและ processor...")
+   try:
+       # Model paths
+       base_model_path = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+       adapter_path = "Aekanun/thai-handwriting-llm"
+
+       # Load processor from base model
+       print("กำลังโหลด processor...")
+       processor = AutoProcessor.from_pretrained(base_model_path, use_auth_token=True)
+
+       # Load base model
+       print("กำลังโหลด base model...")
+       base_model = AutoModelForVision2Seq.from_pretrained(
+           base_model_path,
+           device_map={"": "cpu"},  # ใช้ CPU
+           torch_dtype=torch.float32,  # ใช้ float32 แทน bfloat16
+           trust_remote_code=True,
+           use_auth_token=True
+       )
+
+       # Load adapter
+       print("กำลังโหลด adapter...")
+       model = PeftModel.from_pretrained(
+           base_model,
+           adapter_path,
+           torch_dtype=torch.float32,  # ใช้ float32
+           device_map={"": "cpu"},  # ใช้ CPU
+           use_auth_token=True
+       )
+       
+       print("โหลดโมเดลสำเร็จ!")
+       return True
+   except Exception as e:
+       print(f"เกิดข้อผิดพลาดในการโหลดโมเดล: {str(e)}")
+       return False
+
+def process_handwriting(image):
+   """ฟังก์ชันสำหรับ Gradio interface"""
+   global model, processor
+   
+   if image is None:
+       return "กรุณาอัพโหลดรูปภาพ"
+   
+   try:
+       # Ensure image is in PIL format
+       if not isinstance(image, Image.Image):
+           image = Image.fromarray(image)
+       
+       # Create prompt
+       prompt = """Transcribe the Thai handwritten text from the provided image.
+Only return the transcription in Thai language."""
+       
+       # Create model inputs
+       messages = [
+           {
+               "role": "user",
+               "content": [
+                   {"type": "text", "text": prompt},
+                   {"type": "image", "image": image}
+               ],
+           }
+       ]
+       
+       # Process with model
+       text = processor.apply_chat_template(messages, tokenize=False)
+       inputs = processor(text=text, images=image, return_tensors="pt")
+       
+       # Move inputs to CPU
+       inputs = {k: v.to('cpu') for k, v in inputs.items()}
+       
+       # Generate
+       with torch.no_grad():
+           outputs = model.generate(
+               **inputs,
+               max_new_tokens=256,
+               do_sample=False,
+               pad_token_id=processor.tokenizer.pad_token_id
+           )
+       
+       # Decode output
+       transcription = processor.decode(outputs[0], skip_special_tokens=True)
+       return transcription.strip()
+   except Exception as e:
+       return f"เกิดข้อผิดพลาด: {str(e)}"
+
+# Initialize application
+print("กำลังเริ่มต้นแอปพลิเคชัน...")
+if load_model_and_processor():
+   # Create Gradio interface
+   demo = gr.Interface(
+       fn=process_handwriting,
+       inputs=gr.Image(type="pil", label="อัพโหลดรูปลายมือเขียนภาษาไทย"),
+       outputs=gr.Textbox(label="ข้อความที่แปลงได้"),
+       title="Thai Handwriting Recognition",
+       description="อัพโหลดรูปภาพลายมือเขียนภาษาไทยเพื่อแปลงเป็นข้อความ",
+       examples=[["example1.jpg"], ["example2.jpg"]]
+   )
+   
+   if __name__ == "__main__":
+       demo.launch()
+else:
+   print("ไม่สามารถเริ่มต้นแอปพลิเคชันได้")