from io import BytesIO import string import gradio as gr import requests from utils import Endpoint 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"} 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"} 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 = """

BLIP-2

""" description = """Gradio demo for BLIP-2, a multimodal chatbot from Salesforce Research. To use it, simply upload your image, or click one of the examples to load them. Please visit our project webpage.

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 = "

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models" endpoint = Endpoint() examples = [ ["house.png", "How could someone get out of the house?"], # [ # "sunset.png", # "Write a romantic message that goes along this photo.", # ], ] with gr.Blocks() as iface: state = gr.State([]) gr.Markdown(title) gr.Markdown(description) gr.Markdown(article) with gr.Row(): with gr.Column(): 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=0.8, interactive=True, label="Temperature (set to 0 for greedy decoding with nucleus sampling)", ) len_penalty = gr.Slider( minimum=-2.0, maximum=2.0, value=1.0, step=0.5, interactive=True, label="Length Penalty (larger value encourages longer sequence 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.Row(): caption_output = gr.Textbox(lines=2, 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], ) with gr.Column(): chat_input = gr.Textbox(lines=2, label="Chat Input") with gr.Row(): chatbot = gr.Chatbot() image_input.change(lambda: (None, "", "", []), [], [chatbot, chat_input, caption_output, state]) with gr.Row(): clear_button = gr.Button(value="Clear", interactive=True) clear_button.click( lambda: ("", None, [], []), [], [chat_input, image_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], ) examples = gr.Examples( examples=examples, inputs=[image_input, chat_input, sampling, temperature, len_penalty, rep_penalty, state], outputs=[chatbot, state], run_on_click=True, fn = inference_chat, ) iface.queue(concurrency_count=1, api_open=False, max_size=10) iface.launch(enable_queue=True)