app.py

import os
import torch
import spaces
import gradio as gr
from PIL import Image
from transformers.utils import move_cache
from huggingface_hub import snapshot_download
from transformers import AutoModelForCausalLM, AutoTokenizer


# Load the model and processor

MODEL_PATH = "THUDM/cogvlm2-llama3-chat-19B"

os.environ['HF_HUB_ENABLE_HF_TRANSFER'] = '1'
MODEL_PATH = snapshot_download(MODEL_PATH)
move_cache()

DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8 else torch.float16

tokenizer = AutoTokenizer.from_pretrained(
    MODEL_PATH,
    trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    torch_dtype=TORCH_TYPE,
    trust_remote_code=True,
).to(DEVICE).eval()



text_only_template = """USER: {} ASSISTANT:"""


@spaces.GPU
def generate_caption(image, prompt):
    print(DEVICE)
    
    # Process the image and the prompt
    
    # image = Image.open(image_path).convert('RGB')
    image = image.convert('RGB')
    query = "USER: %s ASSISTANT:" % prompt
    input_by_model = model.build_conversation_input_ids(
        tokenizer,
        query=query,
        history=[],
        images=[image],
        template_version='chat'
    )
    inputs = {
        'input_ids': input_by_model['input_ids'].unsqueeze(0).to(DEVICE),
        'token_type_ids': input_by_model['token_type_ids'].unsqueeze(0).to(DEVICE),
        'attention_mask': input_by_model['attention_mask'].unsqueeze(0).to(DEVICE),
        'images': [[input_by_model['images'][0].to(DEVICE).to(TORCH_TYPE)]] if image is not None else None,
    }
    gen_kwargs = {
            "max_new_tokens": 2048,
            "pad_token_id": 128002,  
    }
    with torch.no_grad():
        outputs = model.generate(**inputs, **gen_kwargs)
        outputs = outputs[:, inputs['input_ids'].shape[1]:]
        response = tokenizer.decode(outputs[0])
        response = response.split("<|end_of_text|>")[0]
        print("\nCogVLM2:", response)
    return response


## make predictions via api ##
# https://www.gradio.app/guides/getting-started-with-the-python-client#connecting-a-general-gradio-app

demo = gr.Interface(
    fn=generate_caption,
    inputs=[gr.Image(type="pil", label="Upload Image"), gr.Textbox(label="Prompt", value="Describe the image in great detail")],
    outputs=gr.Textbox(label="Generated Caption")
)

# Launch the interface
demo.launch(share=True)