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 url = url + "/api/generate" 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 url = url + "/api/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) 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 = """

BLIP-2

""" 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.
Disclaimer: 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 = """Paper: BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
Code: BLIP2 is now integrated into GitHub repo: LAVIS: a One-stop Library for Language and Vision
🤗 `transformers` integration: You can now use `transformers` to use our BLIP-2 models! Check out the official docs

Project Page: BLIP2 on LAVIS
Description: Captioning results from BLIP2_OPT_6.7B. Chat results from BLIP2_FlanT5xxl. """ 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:"], ["pizza.jpg", "What are steps to cook it?"], ["sunset.jpg", "Here is a romantic message going along the photo:"], ["forbidden_city.webp", "In what dynasties was this place built?"], ] with gr.Blocks( css=""" .message.svelte-w6rprc.svelte-w6rprc.svelte-w6rprc {font-size: 20px; margin-top: 20px} #component-21 > div.wrap.svelte-w6rprc {height: 600px;} """ ) 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:\" Use proper punctuation (e.g., question mark).""") 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=1, label="Chat Input") chat_input.submit( inference_chat, [ image_input, chat_input, sampling, temperature, len_penalty, rep_penalty, state, ], [chatbot, state], ) with gr.Row(): clear_button = gr.Button(value="Clear", interactive=True) clear_button.click( lambda: ("", [], []), [], [chat_input, chatbot, state], queue=False, ) 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], queue=False, ) 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)