#!/usr/bin/env python # encoding: utf-8 import spaces import gradio as gr from PIL import Image import traceback import re import torch import argparse from transformers import AutoModel, AutoTokenizer from chat import OmniLMM12B # Load model model_path = 'openbmb/RLAIF-V-12B' model = OmniLMM12B(model_path) ERROR_MSG = "Error, please retry" model_name = 'RLAIF-V-12B' form_radio = { 'choices': ['Beam Search', 'Sampling'], #'value': 'Beam Search', 'value': 'Sampling', 'interactive': True, 'label': 'Decode Type' } # Beam Form num_beams_slider = { 'minimum': 0, 'maximum': 5, 'value': 3, 'step': 1, 'interactive': True, 'label': 'Num Beams' } repetition_penalty_slider = { 'minimum': 0, 'maximum': 3, 'value': 1.2, 'step': 0.01, 'interactive': True, 'label': 'Repetition Penalty' } repetition_penalty_slider2 = { 'minimum': 0, 'maximum': 3, 'value': 1.05, 'step': 0.01, 'interactive': True, 'label': 'Repetition Penalty' } max_new_tokens_slider = { 'minimum': 1, 'maximum': 4096, 'value': 1024, 'step': 1, 'interactive': True, 'label': 'Max New Tokens' } top_p_slider = { 'minimum': 0, 'maximum': 1, 'value': 0.8, 'step': 0.05, 'interactive': True, 'label': 'Top P' } top_k_slider = { 'minimum': 0, 'maximum': 200, 'value': 100, 'step': 1, 'interactive': True, 'label': 'Top K' } temperature_slider = { 'minimum': 0, 'maximum': 2, 'value': 0.7, 'step': 0.05, 'interactive': True, 'label': 'Temperature' } def create_component(params, comp='Slider'): if comp == 'Slider': return gr.Slider( minimum=params['minimum'], maximum=params['maximum'], value=params['value'], step=params['step'], interactive=params['interactive'], label=params['label'] ) elif comp == 'Radio': return gr.Radio( choices=params['choices'], value=params['value'], interactive=params['interactive'], label=params['label'] ) elif comp == 'Button': return gr.Button( value=params['value'], interactive=True ) @spaces.GPU(duration=120) def chat(img, msgs, ctx, params=None, vision_hidden_states=None): if img is None: return -1, "Error, invalid image, please upload a new image", None, None try: image = img.convert('RGB') answer = model.chat( image=image, msgs=msgs, ) return 0, answer, None, None except Exception as err: print(err) traceback.print_exc() return -1, ERROR_MSG, None, None def upload_img(image, _chatbot, _app_session): image = Image.fromarray(image) _app_session['sts']=None _app_session['ctx']=[] _app_session['img']=image _chatbot.append(('', 'Image uploaded successfully, you can talk to me now')) return _chatbot, _app_session def respond(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature): if _app_cfg.get('ctx', None) is None: _chat_bot.append((_question, 'Please upload an image to start')) return '', _chat_bot, _app_cfg _context = _app_cfg['ctx'].copy() if _context: _context.append({"role": "user", "content": _question}) else: _context = [{"role": "user", "content": _question}] print(':', _question) if params_form == 'Beam Search': params = { 'sampling': False, 'num_beams': num_beams, 'repetition_penalty': repetition_penalty, "max_new_tokens": 896 } else: params = { 'sampling': True, 'top_p': top_p, 'top_k': top_k, 'temperature': temperature, 'repetition_penalty': repetition_penalty_2, "max_new_tokens": 896 } code, _answer, _, sts = chat(_app_cfg['img'], _context, None, params) print(':', _answer) _context.append({"role": "assistant", "content": _answer}) _chat_bot.append((_question, _answer)) if code == 0: _app_cfg['ctx']=_context _app_cfg['sts']=sts return '', _chat_bot, _app_cfg def regenerate_button_clicked(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature): if len(_chat_bot) <= 1: _chat_bot.append(('Regenerate', 'No question for regeneration.')) return '', _chat_bot, _app_cfg elif _chat_bot[-1][0] == 'Regenerate': return '', _chat_bot, _app_cfg else: _question = _chat_bot[-1][0] _chat_bot = _chat_bot[:-1] _app_cfg['ctx'] = _app_cfg['ctx'][:-2] return respond(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature) with gr.Blocks() as demo: with gr.Row(): with gr.Column(scale=1, min_width=300): params_form = create_component(form_radio, comp='Radio', visible=True) with gr.Accordion("Beam Search") as beams_according: num_beams = create_component(num_beams_slider) repetition_penalty = create_component(repetition_penalty_slider) with gr.Accordion("Sampling") as sampling_according: top_p = create_component(top_p_slider) top_k = create_component(top_k_slider) temperature = create_component(temperature_slider) repetition_penalty_2 = create_component(repetition_penalty_slider2) regenerate = create_component({'value': 'Regenerate'}, comp='Button') with gr.Column(scale=3, min_width=500): app_session = gr.State({'sts':None,'ctx':None,'img':None}) bt_pic = gr.Image(label="Upload an image to start") chat_bot = gr.Chatbot(label=f"Chat with {model_name}") txt_message = gr.Textbox(label="Input text") regenerate.click( regenerate_button_clicked, [txt_message, chat_bot, app_session, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature], [txt_message, chat_bot, app_session] ) txt_message.submit( respond, [txt_message, chat_bot, app_session, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature], [txt_message, chat_bot, app_session] ) bt_pic.upload(lambda: None, None, chat_bot, queue=False).then(upload_img, inputs=[bt_pic,chat_bot,app_session], outputs=[chat_bot,app_session]) # launch #demo.launch(share=False, debug=True, show_api=False, server_port=8080, server_name="0.0.0.0") demo.launch()