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| import gradio as gr | |
| import subprocess | |
| import torch | |
| from PIL import Image | |
| from transformers import AutoProcessor, AutoModelForCausalLM | |
| # import os | |
| # import random | |
| # from gradio_client import Client | |
| subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) | |
| # Initialize Florence model | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval() | |
| florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True) | |
| # api_key = os.getenv("HF_READ_TOKEN") | |
| article_text = """ | |
| <div style="text-align: center;"> | |
| <p>Enjoying the tool? Buy me a coffee and get exclusive prompt guides!</p> | |
| <p><i>Instantly unlock helpful tips for creating better prompts!</i></p> | |
| <div style="display: flex; justify-content: center;"> | |
| <a href="https://piczify.lemonsqueezy.com/buy/0f5206fa-68e8-42f6-9ca8-4f80c587c83e"> | |
| <img src="https://www.buymeacoffee.com/assets/img/custom_images/yellow_img.png" | |
| alt="Buy Me a Coffee" | |
| style="height: 40px; width: auto; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); border-radius: 10px;"> | |
| </a> | |
| </div> | |
| </div> | |
| """ | |
| def generate_caption(image): | |
| if not isinstance(image, Image.Image): | |
| image = Image.fromarray(image) | |
| inputs = florence_processor(text="<MORE_DETAILED_CAPTION>", images=image, return_tensors="pt").to(device) | |
| generated_ids = florence_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 = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0] | |
| parsed_answer = florence_processor.post_process_generation( | |
| generated_text, | |
| task="<MORE_DETAILED_CAPTION>", | |
| image_size=(image.width, image.height) | |
| ) | |
| prompt = parsed_answer["<MORE_DETAILED_CAPTION>"] | |
| print("\n\nGeneration completed!:"+ prompt) | |
| return prompt | |
| # yield prompt, None | |
| # image_path = generate_image(prompt,random.randint(0, 4294967296)) | |
| # yield prompt, image_path | |
| # def generate_image(prompt, seed=42, width=1024, height=1024): | |
| # try: | |
| # result = Client("KingNish/Realtime-FLUX", hf_token=api_key).predict( | |
| # prompt=prompt, | |
| # seed=seed, | |
| # width=width, | |
| # height=height, | |
| # api_name="/generate_image" | |
| # ) | |
| # # Extract the image path from the result tuple | |
| # image_path = result[0] | |
| # return image_path | |
| # except Exception as e: | |
| # raise Exception(f"Error generating image: {str(e)}") | |
| io = gr.Interface(generate_caption, | |
| inputs=[gr.Image(label="Input Image")], | |
| outputs = [gr.Textbox(label="Output Prompt", lines=2, show_copy_button = True), | |
| # gr.Image(label="Output Image") | |
| ], | |
| #article = article_text | |
| ) | |
| io.launch(debug=True) |