import spaces import argparse from ast import parse import datetime import json import os import time import hashlib import re import gradio as gr import requests import random from filelock import FileLock from io import BytesIO from PIL import Image, ImageDraw, ImageFont from constants import LOGDIR from utils import ( build_logger, server_error_msg, violates_moderation, moderation_msg, load_image_from_base64, get_log_filename, ) from threading import Thread import torch from conversation import Conversation from transformers import AutoModel, AutoTokenizer, TextIteratorStreamer logger = build_logger("gradio_web_server", "gradio_web_server.log") headers = {"User-Agent": "Vintern-Chat Client"} no_change_btn = gr.Button() enable_btn = gr.Button(interactive=True) disable_btn = gr.Button(interactive=False) @spaces.GPU(duration=10) def make_zerogpu_happy(): pass def write2file(path, content): lock = FileLock(f"{path}.lock") with lock: with open(path, "a") as fout: fout.write(content) get_window_url_params = """ function() { const params = new URLSearchParams(window.location.search); url_params = Object.fromEntries(params); console.log(url_params); return url_params; } """ def init_state(state=None): if state is not None: del state return Conversation() def vote_last_response(state, liked, request: gr.Request): conv_data = { "tstamp": round(time.time(), 4), "like": liked, "model": 'Vintern-1B-v3', "state": state.dict(), "ip": request.client.host, } write2file(get_log_filename(), json.dumps(conv_data) + "\n") def upvote_last_response(state, request: gr.Request): logger.info(f"upvote. ip: {request.client.host}") vote_last_response(state, True, request) textbox = gr.MultimodalTextbox(value=None, interactive=True) return (textbox,) + (disable_btn,) * 3 def downvote_last_response(state, request: gr.Request): logger.info(f"downvote. ip: {request.client.host}") vote_last_response(state, False, request) textbox = gr.MultimodalTextbox(value=None, interactive=True) return (textbox,) + (disable_btn,) * 3 def vote_selected_response( state, request: gr.Request, data: gr.LikeData ): logger.info( f"Vote: {data.liked}, index: {data.index}, value: {data.value} , ip: {request.client.host}" ) conv_data = { "tstamp": round(time.time(), 4), "like": data.liked, "index": data.index, "model": 'Vintern-1B-v3', "state": state.dict(), "ip": request.client.host, } write2file(get_log_filename(), json.dumps(conv_data) + "\n") return def flag_last_response(state, request: gr.Request): logger.info(f"flag. ip: {request.client.host}") vote_last_response(state, "flag", request) textbox = gr.MultimodalTextbox(value=None, interactive=True) return (textbox,) + (disable_btn,) * 3 def regenerate(state, image_process_mode, request: gr.Request): logger.info(f"regenerate. ip: {request.client.host}") # state.messages[-1][-1] = None state.update_message(Conversation.ASSISTANT, content='', image=None, idx=-1) prev_human_msg = state.messages[-2] if type(prev_human_msg[1]) in (tuple, list): prev_human_msg[1] = (*prev_human_msg[1][:2], image_process_mode) state.skip_next = False textbox = gr.MultimodalTextbox(value=None, interactive=True) return (state, state.to_gradio_chatbot(), textbox) + (disable_btn,) * 5 def clear_history(request: gr.Request): logger.info(f"clear_history. ip: {request.client.host}") state = init_state() textbox = gr.MultimodalTextbox(value=None, interactive=True) return (state, state.to_gradio_chatbot(), textbox) + (disable_btn,) * 5 def add_text(state, message, system_prompt, request: gr.Request): print(f"state: {state}") if not state: state = init_state() images = message.get("files", []) text = message.get("text", "").strip() logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}") # import pdb; pdb.set_trace() textbox = gr.MultimodalTextbox(value=None, interactive=False) if len(text) <= 0 and len(images) == 0: state.skip_next = True return (state, state.to_gradio_chatbot(), textbox) + (no_change_btn,) * 5 if args.moderate: flagged = violates_moderation(text) if flagged: state.skip_next = True textbox = gr.MultimodalTextbox( value={"text": moderation_msg}, interactive=True ) return (state, state.to_gradio_chatbot(), textbox) + (no_change_btn,) * 5 images = [Image.open(path).convert("RGB") for path in images] if len(images) > 0 and len(state.get_images(source=state.USER)) > 0: state = init_state(state) state.set_system_message(system_prompt) state.append_message(Conversation.USER, text, images) state.skip_next = False return (state, state.to_gradio_chatbot(), textbox) + ( disable_btn, ) * 5 model_name = "5CD-AI/Vintern-1B-v3_5" model = AutoModel.from_pretrained( model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True, ).eval().cuda() tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False) def http_bot( state, temperature, top_p, repetition_penalty, max_new_tokens, max_input_tiles, request: gr.Request, ): logger.info(f"http_bot. ip: {request.client.host}") start_tstamp = time.time() if hasattr(state, "skip_next") and state.skip_next: # This generate call is skipped due to invalid inputs yield ( state, state.to_gradio_chatbot(), gr.MultimodalTextbox(interactive=False), ) + (no_change_btn,) * 5 return # No available worker if model is None: # state.messages[-1][-1] = server_error_msg state.update_message(Conversation.ASSISTANT, server_error_msg) yield ( state, state.to_gradio_chatbot(), gr.MultimodalTextbox(interactive=False), disable_btn, disable_btn, disable_btn, enable_btn, enable_btn, ) return all_images = state.get_images(source=state.USER) all_image_paths = [state.save_image(image) for image in all_images] state.append_message(Conversation.ASSISTANT, state.streaming_placeholder) yield ( state, state.to_gradio_chatbot(), gr.MultimodalTextbox(interactive=False), ) + (disable_btn,) * 5 try: # Stream output # response = requests.post(worker_addr, json=pload, headers=headers, stream=True, timeout=300) streamer = TextIteratorStreamer( tokenizer, skip_prompt=True, skip_special_tokens=True ) generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024) thread = Thread(target=model.generate, kwargs=generation_kwargs) thread.start() buffer = "" for new_text in streamer: buffer += new_text # Remove <|im_end|> or similar tokens from the output buffer = buffer.replace("<|im_end|>", "") state.update_message(Conversation.ASSISTANT, buffer + state.streaming_placeholder, None) yield ( state, state.to_gradio_chatbot(), gr.MultimodalTextbox(interactive=False), ) + (disable_btn,) * 5 except Exception as e: state.update_message(Conversation.ASSISTANT, server_error_msg, None) yield ( state, state.to_gradio_chatbot(), gr.MultimodalTextbox(interactive=True), ) + ( disable_btn, disable_btn, disable_btn, enable_btn, enable_btn, ) return ai_response = state.return_last_message() logger.info(f"==== response ====\n{ai_response}") state.end_of_current_turn() yield ( state, state.to_gradio_chatbot(), gr.MultimodalTextbox(interactive=True), ) + (enable_btn,) * 5 finish_tstamp = time.time() logger.info(f"{buffer}") data = { "tstamp": round(finish_tstamp, 4), "like": None, "model": model_name, "start": round(start_tstamp, 4), "finish": round(start_tstamp, 4), "state": state.dict(), "images": all_image_paths, "ip": request.client.host, } write2file(get_log_filename(), json.dumps(data) + "\n") #

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

title_html = """

Vintern-1B: Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

[🆕 InternVL Blog] [📖 InternVL Paper] [🌟 Github]
[📜 Document] [🗨ī¸ Official Demo] """ tos_markdown = """ ### Terms of use By using this service, users are required to agree to the following terms: The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research. Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator. For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality. """ # .gradio-container {margin: 5px 10px 0 10px !important}; block_css = """ .gradio-container {margin: 0.1% 1% 0 1% !important; max-width: 98% !important;}; #buttons button { min-width: min(120px,100%); } .gradient-text { font-size: 28px; width: auto; font-weight: bold; background: linear-gradient(45deg, red, orange, yellow, green, blue, indigo, violet); background-clip: text; -webkit-background-clip: text; color: transparent; } .plain-text { font-size: 22px; width: auto; font-weight: bold; } """ js = """ function createWaveAnimation() { const text = document.getElementById('text'); var i = 0; setInterval(function() { const colors = [ 'red, orange, yellow, green, blue, indigo, violet, purple', 'orange, yellow, green, blue, indigo, violet, purple, red', 'yellow, green, blue, indigo, violet, purple, red, orange', 'green, blue, indigo, violet, purple, red, orange, yellow', 'blue, indigo, violet, purple, red, orange, yellow, green', 'indigo, violet, purple, red, orange, yellow, green, blue', 'violet, purple, red, orange, yellow, green, blue, indigo', 'purple, red, orange, yellow, green, blue, indigo, violet', ]; const angle = 45; const colorIndex = i % colors.length; text.style.background = `linear-gradient(${angle}deg, ${colors[colorIndex]})`; text.style.webkitBackgroundClip = 'text'; text.style.backgroundClip = 'text'; text.style.color = 'transparent'; text.style.fontSize = '28px'; text.style.width = 'auto'; text.textContent = 'Vintern-1B'; text.style.fontWeight = 'bold'; i += 1; }, 200); const params = new URLSearchParams(window.location.search); url_params = Object.fromEntries(params); // console.log(url_params); // console.log('hello world...'); // console.log(window.location.search); // console.log('hello world...'); // alert(window.location.search) // alert(url_params); return url_params; } """ def build_demo(): textbox = gr.MultimodalTextbox( interactive=True, file_types=["image", "video"], placeholder="Enter message or upload file...", show_label=False, ) with gr.Blocks( title="Vintern-Chat", theme=gr.themes.Default(), css=block_css, ) as demo: state = gr.State() with gr.Row(): with gr.Column(scale=2): # gr.Image('./gallery/logo-47b364d3.jpg') gr.HTML(title_html) with gr.Accordion("Settings", open=False) as setting_row: system_prompt = gr.Textbox( value="č¯ˇå°Ŋ可čƒŊč¯Ļįģ†åœ°å›žį­”į”¨æˆˇįš„é—Žéĸ˜ã€‚", label="System Prompt", interactive=True, ) temperature = gr.Slider( minimum=0.0, maximum=1.0, value=0.2, step=0.1, interactive=True, label="Temperature", ) top_p = gr.Slider( minimum=0.0, maximum=1.0, value=0.7, step=0.1, interactive=True, label="Top P", ) repetition_penalty = gr.Slider( minimum=1.0, maximum=1.5, value=1.1, step=0.02, interactive=True, label="Repetition penalty", ) max_output_tokens = gr.Slider( minimum=0, maximum=4096, value=1024, step=64, interactive=True, label="Max output tokens", ) max_input_tiles = gr.Slider( minimum=1, maximum=32, value=12, step=1, interactive=True, label="Max input tiles (control the image size)", ) examples = gr.Examples( examples=[ [ { "files": [ "gallery/14.jfif", ], "text": "Please help me analyze this picture.", } ], [ { "files": [ "gallery/1-2.PNG", ], "text": "Implement this flow chart using python", } ], [ { "files": [ "gallery/15.PNG", ], "text": "Please help me analyze this picture.", } ], ], inputs=[textbox], ) with gr.Column(scale=8): chatbot = gr.Chatbot( elem_id="chatbot", label="InternVL", height=580, show_copy_button=True, show_share_button=True, avatar_images=[ "assets/human.png", "assets/assistant.png", ], bubble_full_width=False, ) with gr.Row(): with gr.Column(scale=8): textbox.render() with gr.Column(scale=1, min_width=50): submit_btn = gr.Button(value="Send", variant="primary") with gr.Row(elem_id="buttons") as button_row: upvote_btn = gr.Button(value="👍 Upvote", interactive=False) downvote_btn = gr.Button(value="👎 Downvote", interactive=False) flag_btn = gr.Button(value="⚠ī¸ Flag", interactive=False) # stop_btn = gr.Button(value="⏚ī¸ Stop Generation", interactive=False) regenerate_btn = gr.Button( value="🔄 Regenerate", interactive=False ) clear_btn = gr.Button(value="🗑ī¸ Clear", interactive=False) gr.Markdown(tos_markdown) url_params = gr.JSON(visible=False) # Register listeners btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn] upvote_btn.click( upvote_last_response, [state], [textbox, upvote_btn, downvote_btn, flag_btn], ) downvote_btn.click( downvote_last_response, [state], [textbox, upvote_btn, downvote_btn, flag_btn], ) chatbot.like( vote_selected_response, [state], [], ) flag_btn.click( flag_last_response, [state], [textbox, upvote_btn, downvote_btn, flag_btn], ) regenerate_btn.click( regenerate, [state, system_prompt], [state, chatbot, textbox] + btn_list, ).then( http_bot, [ state, temperature, top_p, repetition_penalty, max_output_tokens, max_input_tiles, ], [state, chatbot, textbox] + btn_list, ) clear_btn.click(clear_history, None, [state, chatbot, textbox] + btn_list) textbox.submit( add_text, [state, textbox, system_prompt], [state, chatbot, textbox] + btn_list, ).then( http_bot, [ state, temperature, top_p, repetition_penalty, max_output_tokens, max_input_tiles, ], [state, chatbot, textbox] + btn_list, ) submit_btn.click( add_text, [state, textbox, system_prompt], [state, chatbot, textbox] + btn_list, ).then( http_bot, [ state, temperature, top_p, repetition_penalty, max_output_tokens, max_input_tiles, ], [state, chatbot, textbox] + btn_list, ) return demo if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--host", type=str, default="0.0.0.0") parser.add_argument("--port", type=int, default=7860) parser.add_argument("--concurrency-count", type=int, default=10) parser.add_argument("--share", action="store_true") parser.add_argument("--moderate", action="store_true") args = parser.parse_args() logger.info(f"args: {args}") logger.info(args) demo = build_demo() demo.queue(api_open=False).launch( server_name=args.host, server_port=args.port, share=args.share, max_threads=args.concurrency_count, )