fix

app.py

@@ -1,103 +1,33 @@
-import gradio as gr
-import torch
-from transformers import AutoModelForVision2Seq, AutoProcessor, BitsAndBytesConfig
-from PIL import Image
+import subprocess
+import sys
 
-# Global variables for model and processor
-model = None
-processor = None
+def install_packages():
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', '--upgrade', 'pip'])
+    with open('requirements.txt') as f:
+        packages = f.read().splitlines()
+    for package in packages:
+        if package and not package.startswith('#'):
+            subprocess.check_call([sys.executable, '-m', 'pip', 'install', package])
 
-def load_model_and_processor():
-    global model, processor
+if __name__ == "__main__":
+    print("Installing required packages...")
+    install_packages()
     
-    model_path = "Aekanun/thai-handwriting-llm"
-    base_model_path = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+    import gradio as gr
+    import torch
+    from transformers import AutoProcessor
     
-    # BitsAndBytes config for 4-bit quantization
-    bnb_config = BitsAndBytesConfig(
-        load_in_4bit=True,
-        bnb_4bit_use_double_quant=True,
-        bnb_4bit_quant_type="nf4",
-        bnb_4bit_compute_dtype=torch.bfloat16
-    )
-    
-    try:
-        # Load processor from base model
-        processor = AutoProcessor.from_pretrained(base_model_path)
-        
-        # Load fine-tuned model
-        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"
+    def process_handwriting(image):
+        if image is None:
+            return "กรุณาอัพโหลดรูปภาพ"
+        return f"ทดสอบระบบ: Torch version: {torch.__version__}, Transformers installed"
     
-    try:
-        # Ensure image is in PIL format
-        if not isinstance(image, Image.Image):
-            image = Image.fromarray(image)
-        
-        # Prepare prompt and messages
-        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}
-                ],
-            }
-        ]
-        
-        # Process input
-        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()}
-        
-        # Generate output
-        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
-    
-    except Exception as e:
-        return f"เกิดข้อผิดพลาด: {str(e)}"
-
-# Load model when starting
-print("กำลังโหลดโมเดล...")
-model_loaded = load_model_and_processor()
-
-if model_loaded:
-    # Create 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 ด้วย LLaMA Vision",
-        description="อัพโหลดรูปภาพลายมือเขียนภาษาไทยเพื่อแปลงเป็นข้อความ โดยใช้โมเดล LLaMA Vision ที่ fine-tune มาสำหรับภาษาไทย",
-        examples=[["example1.jpg"], ["example2.jpg"]]
+        inputs=gr.Image(type="pil", label="อัพโหลดรูปภาพ"),
+        outputs=gr.Textbox(label="ผลลัพธ์"),
+        title="Test Installation",
+        description="ทดสอบการติดตั้ง libraries"
     )
     
-    if __name__ == "__main__":
-        demo.launch(share=True)
-else:
-    print("ไม่สามารถโหลดโมเดลได้ กรุณาตรวจสอบ log")
+    demo.launch()

requirements.txt

@@ -1,7 +1,8 @@
---find-links https://download.pytorch.org/whl/torch_stable.html
-torch==2.1.1+cpu
-transformers==4.35.2
-gradio==4.13.0
-Pillow==10.1.0
-bitsandbytes==0.41.1
-accelerate==0.25.0
+--extra-index-url https://download.pytorch.org/whl/cpu
+huggingface_hub
+transformers
+torch
+gradio
+Pillow
+bitsandbytes
+accelerate