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image-to-prompt/README.md

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----
-title: Image To Prompt
-emoji: 😻
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 4.44.1
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

image-to-prompt/app.py

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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")
-
-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")
-                            ]
-                 )
-io.launch(debug=True)

image-to-prompt/requirements.txt

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-transformers
-timm