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from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
import gradio as gr
model_id = 'microsoft/Florence-2-large'
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True,
torch_dtype="auto",
#device_map="auto",
cache_dir="./cache",
#attn_implementation="flash_attention_2",
).eval()
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True,
torch_dtype="auto",
#device_map="auto",
cache_dir="./cache",
#attn_implementation="flash_attention_2",
)
def run_example(task_prompt, image, text_input=None):
if text_input is None:
prompt = task_prompt
else:
prompt = task_prompt + text_input
inputs = processor(text=prompt, images=image, return_tensors="pt")
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
early_stopping=False,
do_sample=False,
num_beams=3,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = processor.post_process_generation(
generated_text,
task=task_prompt,
image_size=(image.width, image.height),
#stream=True
)
return parsed_answer
def inference(image, task_prompt, text_input):
return run_example(task_prompt, image, text_input)
interface = gr.Interface(
fn=inference,
inputs=[
gr.Image(type="pil"),
gr.Textbox(label="Task Prompt", placeholder="Enter task prompt here"),
gr.Textbox(label="Additional Text Input", placeholder="Enter additional text input here (optional)", optional=True)
],
outputs="text",
title="Hugging Face Model Inference",
description="Generate text based on an image and a prompt using a Hugging Face model"
)
if __name__ == "__main__":
interface.launch()
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