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| from io import BytesIO | |
| import string | |
| import gradio as gr | |
| import requests | |
| from utils import Endpoint, get_token | |
| def encode_image(image): | |
| buffered = BytesIO() | |
| image.save(buffered, format="JPEG") | |
| buffered.seek(0) | |
| return buffered | |
| def query_chat_api( | |
| image, prompt, decoding_method, temperature, len_penalty, repetition_penalty | |
| ): | |
| url = endpoint.url | |
| headers = { | |
| "User-Agent": "BLIP-2 HuggingFace Space", | |
| "Auth-Token": get_token(), | |
| } | |
| data = { | |
| "prompt": prompt, | |
| "use_nucleus_sampling": decoding_method == "Nucleus sampling", | |
| "temperature": temperature, | |
| "length_penalty": len_penalty, | |
| "repetition_penalty": repetition_penalty, | |
| } | |
| image = encode_image(image) | |
| files = {"image": image} | |
| response = requests.post(url, data=data, files=files, headers=headers) | |
| if response.status_code == 200: | |
| return response.json() | |
| else: | |
| return "Error: " + response.text | |
| def query_caption_api( | |
| image, decoding_method, temperature, len_penalty, repetition_penalty | |
| ): | |
| url = endpoint.url | |
| # replace /generate with /caption | |
| url = url.replace("/generate", "/caption") | |
| headers = { | |
| "User-Agent": "BLIP-2 HuggingFace Space", | |
| "Auth-Token": get_token(), | |
| } | |
| data = { | |
| "use_nucleus_sampling": decoding_method == "Nucleus sampling", | |
| "temperature": temperature, | |
| "length_penalty": len_penalty, | |
| "repetition_penalty": repetition_penalty, | |
| } | |
| image = encode_image(image) | |
| files = {"image": image} | |
| response = requests.post(url, data=data, files=files, headers=headers) | |
| if response.status_code == 200: | |
| return response.json() | |
| else: | |
| return "Error: " + response.text | |
| def postprocess_output(output): | |
| # if last character is not a punctuation, add a full stop | |
| if not output[0][-1] in string.punctuation: | |
| output[0] += "." | |
| return output | |
| def inference_chat( | |
| image, | |
| text_input, | |
| decoding_method, | |
| temperature, | |
| length_penalty, | |
| repetition_penalty, | |
| history=[], | |
| ): | |
| text_input = text_input | |
| history.append(text_input) | |
| prompt = " ".join(history) | |
| print(prompt) | |
| output = query_chat_api( | |
| image, prompt, decoding_method, temperature, length_penalty, repetition_penalty | |
| ) | |
| output = postprocess_output(output) | |
| history += output | |
| chat = [ | |
| (history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) | |
| ] # convert to tuples of list | |
| return {chatbot: chat, state: history} | |
| def inference_caption( | |
| image, | |
| decoding_method, | |
| temperature, | |
| length_penalty, | |
| repetition_penalty, | |
| ): | |
| output = query_caption_api( | |
| image, decoding_method, temperature, length_penalty, repetition_penalty | |
| ) | |
| return output[0] | |
| title = """<h1 align="center">BLIP-2</h1>""" | |
| description = """Gradio demo for BLIP-2, image-to-text generation from Salesforce Research. To use it, simply upload your image, or click one of the examples to load them. | |
| <br> <strong>Disclaimer</strong>: This is a research prototype and is not intended for production use. No data including but not restricted to text and images is collected.""" | |
| article = """<strong>Paper</strong>: <a href='https://arxiv.org/abs/2301.12597' target='_blank'>BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models</a> | |
| <br> <strong>Code</strong>: BLIP2 is now integrated into GitHub repo: <a href='https://github.com/salesforce/LAVIS' target='_blank'>LAVIS: a One-stop Library for Language and Vision</a> | |
| <br> <strong>Project Page</strong>: <a href='https://github.com/salesforce/LAVIS/tree/main/projects/blip2' target='_blank'> BLIP2 on LAVIS</a> | |
| <br> <strong>Description</strong>: Captioning results from <strong>BLIP2_OPT_6.7B</strong>. Chat results from <strong>BLIP2_FlanT5xxl</strong>. | |
| """ | |
| endpoint = Endpoint() | |
| examples = [ | |
| ["house.png", "How could someone get out of the house?"], | |
| ["flower.jpg", "Question: What is this flower and where is it's origin? Answer:"], | |
| ["forbidden_city.webp", "In what dynasties was this place built?"], | |
| ["pizza.jpg", "What are the ingredients of it?"], | |
| ] | |
| with gr.Blocks( | |
| css=""".message.svelte-w6rprc.svelte-w6rprc.svelte-w6rprc {font-size: 20px; margin-top: 20px}""" | |
| ) as iface: | |
| state = gr.State([]) | |
| gr.Markdown(title) | |
| gr.Markdown(description) | |
| gr.Markdown(article) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| image_input = gr.Image(type="pil") | |
| # with gr.Row(): | |
| sampling = gr.Radio( | |
| choices=["Beam search", "Nucleus sampling"], | |
| value="Beam search", | |
| label="Text Decoding Method", | |
| interactive=True, | |
| ) | |
| temperature = gr.Slider( | |
| minimum=0.5, | |
| maximum=1.0, | |
| value=1.0, | |
| step=0.1, | |
| interactive=True, | |
| label="Temperature (used with nucleus sampling)", | |
| ) | |
| len_penalty = gr.Slider( | |
| minimum=-1.0, | |
| maximum=2.0, | |
| value=1.0, | |
| step=0.2, | |
| interactive=True, | |
| label="Length Penalty (set to larger for longer sequence, used with beam search)", | |
| ) | |
| rep_penalty = gr.Slider( | |
| minimum=1.0, | |
| maximum=5.0, | |
| value=1.5, | |
| step=0.5, | |
| interactive=True, | |
| label="Repeat Penalty (larger value prevents repetition)", | |
| ) | |
| with gr.Column(scale=1.8): | |
| with gr.Column(): | |
| caption_output = gr.Textbox(lines=1, label="Caption Output") | |
| caption_button = gr.Button( | |
| value="Caption it!", interactive=True, variant="primary" | |
| ) | |
| caption_button.click( | |
| inference_caption, | |
| [ | |
| image_input, | |
| sampling, | |
| temperature, | |
| len_penalty, | |
| rep_penalty, | |
| ], | |
| [caption_output], | |
| ) | |
| gr.Markdown("""Trying prompting your input for chat; e.g. example prompt for QA, \"Question: {} Answer:\"""") | |
| with gr.Row(): | |
| with gr.Column( | |
| scale=1.5, | |
| ): | |
| chatbot = gr.Chatbot( | |
| label="Chat Output (from FlanT5)", | |
| ) | |
| # with gr.Row(): | |
| with gr.Column(scale=1): | |
| chat_input = gr.Textbox(lines=2, label="Chat Input") | |
| with gr.Row(): | |
| clear_button = gr.Button(value="Clear", interactive=True) | |
| clear_button.click( | |
| lambda: ("", [], []), | |
| [], | |
| [chat_input, chatbot, state], | |
| ) | |
| submit_button = gr.Button( | |
| value="Submit", interactive=True, variant="primary" | |
| ) | |
| submit_button.click( | |
| inference_chat, | |
| [ | |
| image_input, | |
| chat_input, | |
| sampling, | |
| temperature, | |
| len_penalty, | |
| rep_penalty, | |
| state, | |
| ], | |
| [chatbot, state], | |
| ) | |
| image_input.change( | |
| lambda: ("", "", []), | |
| [], | |
| [chatbot, caption_output, state] | |
| ) | |
| examples = gr.Examples( | |
| examples=examples, | |
| inputs=[image_input, chat_input], | |
| ) | |
| iface.queue(concurrency_count=1, api_open=False, max_size=10) | |
| iface.launch(enable_queue=True) | |