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| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") | |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda") | |
| hf_token = os.environ.get('HF_TOKEN') | |
| from gradio_client import Client | |
| client = Client("https://fffiloni-test-llama-api.hf.space/", hf_token=hf_token) | |
| def infer(image_input): | |
| #img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' | |
| raw_image = Image.open(image_input).convert('RGB') | |
| # unconditional image captioning | |
| inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16) | |
| out = model.generate(**inputs) | |
| caption = processor.decode(out[0], skip_special_tokens=True) | |
| print(caption) | |
| llama_q = f""" | |
| I'll give you a simple image caption, from i want you to provide a story that would fit well with the image. | |
| Here's the music description : | |
| {caption} | |
| """ | |
| result = client.predict( | |
| llama_q, # str in 'Message' Textbox component | |
| api_name="/predict" | |
| ) | |
| print(f"Llama2 result: {result}") | |
| return caption, result | |
| css=""" | |
| #col-container {max-width: 910px; margin-left: auto; margin-right: auto;} | |
| a {text-decoration-line: underline; font-weight: 600;} | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown( | |
| """ | |
| # Image to Story | |
| Upload an image, get a story ! | |
| <br/> | |
| <br/> | |
| [](https://huggingface.co./spaces/fffiloni/SplitTrack2MusicGen?duplicate=true) for longer audio, more control and no queue.</p> | |
| """ | |
| ) | |
| image_in = gr.Image(label="Image input", type="filepath") | |
| submit_btn = gr.Button('Sumbit') | |
| caption = gr.Textbox(label="Generated Caption") | |
| story = gr.Textbox(label="generated Story") | |
| submit_btn.click(fn=infer, inputs=[image_in], outputs=[caption, story]) | |
| demo.queue().launch() | |