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| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration | |
| model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b") | |
| processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b") | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| import os | |
| 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') | |
| prompt = "Can you please describe what's happening in the image, and give information about the characters and the place ?" | |
| inputs = processor(images=raw_image, text=prompt, return_tensors="pt").to(device) | |
| outputs = model.generate( | |
| **inputs, | |
| do_sample=False, | |
| num_beams=5, | |
| max_length=256, | |
| min_length=1, | |
| top_p=0.9, | |
| repetition_penalty=1.5, | |
| length_penalty=1.0, | |
| temperature=1, | |
| ) | |
| generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip() | |
| print(generated_text) | |
| 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: | |
| '{generated_text}' | |
| """ | |
| result = client.predict( | |
| llama_q, # str in 'Message' Textbox component | |
| api_name="/predict" | |
| ) | |
| print(f"Llama2 result: {result}") | |
| return generated_text, 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() | |