Update app.py

app.py

@@ -4,65 +4,52 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 from PIL import Image
 import requests
 import gradio as gr
-import spaces  # Import Hugging Face Spaces package
+import spaces
 
 # Load model and tokenizer
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
 model_name = 'scb10x/llama-3-typhoon-v1.5-8b-instruct-vision-preview'
 
-@spaces.GPU(duration=60)  # Decorate the function to dynamically request and release GPU
+@spaces.GPU(duration=120)  # ใช้ GPU เป็นเวลา 120 วินาที
 def load_model():
     model = AutoModelForCausalLM.from_pretrained(
         model_name,
-        revision='main',  # Or a specific commit hash
-        torch_dtype=torch.float16 if device == 'cuda' else torch.float32,
+        torch_dtype=torch.float16,
         device_map='auto',
         trust_remote_code=True
     )
-
     return model
 
 model = load_model()
-
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 
-def prepare_inputs(text, image, device='cuda'):
+def prepare_inputs(text, image):
     messages = [
         {"role": "system", "content": "You are a helpful vision-capable assistant who eagerly converses with the user in their language."},
+        {"role": "user", "content": f"<image>\n{text}"}
     ]
-    messages.append({"role": "user", "content": "<|image|>\n" + text})
-
-    inputs_formatted = tokenizer.apply_chat_template(
-        messages,
-        add_generation_prompt=True,
-        tokenize=False
-    )
-
-    text_chunks = [tokenizer(chunk).input_ids for chunk in inputs_formatted.split('<|image|>')]
-    input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0).to(device)
-    attention_mask = torch.ones_like(input_ids).to(device)
+    inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+    return inputs
 
-    return input_ids, attention_mask
-
-@spaces.GPU(duration=60)  # Decorate the function for GPU use
+@spaces.GPU(duration=60)  # ใช้ GPU เป็นเวลา 60 วินาที
 def predict(prompt, img_url):
     try:
-        image = Image.open(requests.get(img_url, stream=True).raw)
-        image_tensor = model.process_images([image], model.config).to(dtype=model.dtype, device=device)
-
-        input_ids, attention_mask = prepare_inputs(prompt, image, device=device)
-
+        image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+        image = image.resize((model.config.image_size, model.config.image_size))
+        image_tensor = model.preprocess_images([image]).to(model.device)
+        
+        inputs = prepare_inputs(prompt, image)
+        
         output_ids = model.generate(
-            input_ids,
+            inputs,
             images=image_tensor,
             max_new_tokens=100,
-            use_cache=True,
+            do_sample=True,
             temperature=0.2,
             top_p=0.2,
             repetition_penalty=1.0
         )[0]
-
-        result = tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()
+        
+        result = tokenizer.decode(output_ids[inputs.shape[1]:], skip_special_tokens=True).strip()
         return result
     except Exception as e:
         return str(e)
@@ -72,10 +59,12 @@ inputs = [
     gr.Textbox(label="Prompt", placeholder="Ask about the food in the image"),
     gr.Textbox(label="Image URL", placeholder="Enter an image URL")
 ]
-
 outputs = gr.Textbox(label="Generated Output")
 
 gr.Interface(
-    fn=predict, inputs=inputs, outputs=outputs, title="Food Image AI Assistant",
+    fn=predict, 
+    inputs=inputs, 
+    outputs=outputs, 
+    title="Food Image AI Assistant",
     description="This model can analyze food images and answer questions about them."
-).launch()
+).launch()