Spaces:
Build error
Build error
File size: 1,888 Bytes
789acc7 fd950ef 789acc7 fd950ef 789acc7 5ee7893 fd950ef 789acc7 fd950ef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
import gradio as gr
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import warnings
# disable some warnings
transformers.logging.set_verbosity_error()
transformers.logging.disable_progress_bar()
warnings.filterwarnings('ignore')
# set device
torch.set_default_device('cuda') # or 'cpu'
model_name = 'cognitivecomputations/dolphin-vision-7b'
# create model
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map='auto',
trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True)
def inference(prompt, image):
messages = [
{"role": "user", "content": f'<image>\n{prompt}'}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)
image_tensor = model.process_images([image], model.config).to(dtype=model.dtype)
# generate
output_ids = model.generate(
input_ids,
images=image_tensor,
max_new_tokens=2048,
use_cache=True)[0]
return tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
prompt_input = gr.Textbox(label="Prompt", placeholder="Describe this image in detail")
image_input = gr.Image(label="Image", type="pil")
submit_button = gr.Button("Submit")
with gr.Column():
output_text = gr.Textbox(label="Output")
submit_button.click(fn=inference, inputs=[prompt_input, image_input], outputs=output_text)
demo.launch() |