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import gradio as gr
import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration
from transformers import BitsAndBytesConfig
from sentence_transformers import SentenceTransformer, util
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16
)
embedder = SentenceTransformer('all-mpnet-base-v2')
model_id = "llava-hf/llava-1.5-7b-hf"
processor = AutoProcessor.from_pretrained(model_id)
model = LlavaForConditionalGeneration.from_pretrained(
model_id,
quantization_config=quantization_config,
device_map="auto",
use_flash_attention_2=True,
low_cpu_mem_usage=True
)
def text_to_image(image, prompt):
prompt = f'USER: <image>\n{prompt}\nASSISTANT:'
inputs = processor([prompt], images=[image], padding=True, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=500)
generated_text = processor.batch_decode(output, skip_special_tokens=True)
text = generated_text.pop()
text_output = text.split("ASSISTANT:")[-1]
text_embeddings = embedder.encode(text_output)
return text_output, dict(text_embeddings=text_embeddings)
demo = gr.Interface(
fn=text_to_image,
inputs=[
gr.Image(label='Select an image to analyze', type='pil'),
gr.Textbox(label='Enter Prompt')
],
outputs=[gr.Textbox(label='Maurice says:'), gr.JSON(label='Embedded text')]
)
if __name__ == "__main__":
demo.launch(show_api=False)
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