import os import gradio as gr import openai import requests import csv import argparse from models.vlog import Vlogger parser = argparse.ArgumentParser() parser.add_argument('--video_path', default='examples/huaqiang.mp4') parser.add_argument('--alpha', default=10, type=int, help='Determine the maximum segment number for KTS algorithm, the larger the value, the fewer segments.') parser.add_argument('--beta', default=1, type=int, help='The smallest time gap between successive clips, in seconds.') parser.add_argument('--data_dir', default='./examples', type=str, help='Directory for saving videos and logs.') parser.add_argument('--tmp_dir', default='./tmp', type=str, help='Directory for saving intermediate files.') # * Models settings * parser.add_argument('--openai_api_key', default='xxx', type=str, help='OpenAI API key') parser.add_argument('--image_caption', action='store_true', dest='image_caption', default=True, help='Set this flag to True if you want to use BLIP Image Caption') parser.add_argument('--dense_caption', action='store_true', dest='dense_caption', default=True, help='Set this flag to True if you want to use Dense Caption') parser.add_argument('--feature_extractor', default='openai/clip-vit-base-patch32', help='Select the feature extractor model for video segmentation') parser.add_argument('--feature_extractor_device', choices=['cuda', 'cpu'], default='cuda', help='Select the device: cuda or cpu') parser.add_argument('--image_captioner', choices=['blip', 'blip2'], dest='captioner_base_model', default='blip2', help='blip2 requires 15G GPU memory, blip requires 6G GPU memory') parser.add_argument('--image_captioner_device', choices=['cuda', 'cpu'], default='cuda', help='Select the device: cuda or cpu, gpu memory larger than 14G is recommended') parser.add_argument('--dense_captioner_device', choices=['cuda', 'cpu'], default='cuda', help='Select the device: cuda or cpu, < 6G GPU is not recommended>') parser.add_argument('--audio_translator', default='large') parser.add_argument('--audio_translator_device', choices=['cuda', 'cpu'], default='cuda') parser.add_argument('--gpt_version', choices=['gpt-3.5-turbo'], default='gpt-3.5-turbo') args = parser.parse_args() def get_empty_state(): return {"total_tokens": 0, "messages": []} def submit_api_key_fn(api_key, vlogger): try: vlogger.init_llm_with_api_key(api_key) return gr.update(value = "OpenAI key submitted successful 🎉"), True, vlogger except Exception as e: return gr.update(value = f"Error {e}"), False, vlogger def submit_message(prompt, state, vlogger, api_key_submitted, vlog_loaded): if not api_key_submitted: return gr.update(value=''), [("👀", "Please enter your OpenAI API key 😊"),], state, vlogger if not vlog_loaded: return gr.update(value=''), [("👀", "Please follow the instruction to select a video and generate the document for chatting 😊"),], state, vlogger history = state['messages'] if not prompt: return gr.update(value=''), [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], state, vlogger prompt_msg = { "role": "user", "content": prompt } try: history.append(prompt_msg) answer = vlogger.chat2video(prompt) history.append({"role": "system", "content": answer}) except Exception as e: history.append(prompt_msg) history.append({ "role": "system", "content": f"Error: {e}" }) chat_messages = [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)] return '', chat_messages, state, vlogger def clear_conversation(vlogger): vlogger.clean_history() # return input_message, video_inp, chatbot, vlog_outp, state, vlogger, vlog_loaded return gr.update(value=None, visible=True), gr.update(value=None, interactive=False), None, gr.update(value=None, visible=True), get_empty_state(), vlogger, False def vlog_fn(vid_path, vlogger, api_key_submitted): if not api_key_submitted: log_text = "====== Please enter your OpenAI API key first 😊 =====" return gr.update(value=log_text, visible=True), False, vlogger print(vid_path) if vid_path is None: log_text = "====== Please select an video from examples first 🤔 =====" vloaded_flag = False else: log_list = vlogger.video2log(vid_path) log_text = "\n".join(log_list) vloaded_flag = True return gr.update(value=log_text, visible=True), vloaded_flag, vlogger css = """ #col-container {max-width: 90%; margin-left: auto; margin-right: auto;} #video_inp {min-height: 300px} #chatbox {min-height: 100px;} #header {text-align: center; #hint {font-size: 0.9em; padding: 0.5em; margin: 0;} .message { font-size: 1.2em; } """ with gr.Blocks(css=css) as demo: state = gr.State(get_empty_state()) vlogger = gr.State(Vlogger(args)) vlog_loaded = gr.State(False) api_key_submitted = gr.State(False) with gr.Column(elem_id="col-container"): gr.Markdown("""## 🎞️ VLog Demo Powered by BLIP2, GRIT, Whisper, ChatGPT and LangChain Github: [https://github.com/showlab/VLog](https://github.com/showlab/VLog)""", elem_id="header") gr.Markdown("*Instruction*: For the current demo, please enter OpenAI api key, select an example video, click the button to generate a document and try chatting over the video 😊", elem_id="hint") with gr.Row(): with gr.Column(scale=6): video_inp = gr.Video(label="video_input", interactive=False) chatbot = gr.Chatbot(elem_id="chatbox") input_message = gr.Textbox(show_label=False, placeholder="Enter text and press enter", visible=True).style(container=False) btn_submit = gr.Button("Submit") btn_clear_conversation = gr.Button("🔃 Start New Conversation") with gr.Column(scale=6): vlog_btn = gr.Button("Generate Video Document") vlog_outp = gr.Textbox(label="Document output", lines=30) with gr.Column(scale=1): openai_api_key = gr.Textbox( placeholder="Input OpenAI API key and press Enter", show_label=False, label = "OpenAI API Key", lines=1, type="password" ) examples = gr.Examples( examples=[ ["examples/basketball_vlog.mp4"], ["examples/travel_in_roman.mp4"], ["examples/C8lMW0MODFs.mp4"], ["examples/outcGtbnMuQ.mp4"], ["examples/huaqiang.mp4"], ], inputs=[video_inp], ) gr.HTML('''


You can duplicate this Space to skip the queue:Duplicate Space
''') btn_submit.click(submit_message, [input_message, state, vlogger, api_key_submitted, vlog_loaded], [input_message, chatbot, state, vlogger]) input_message.submit(submit_message, [input_message, state, vlogger, api_key_submitted, vlog_loaded], [input_message, chatbot, state, vlogger]) btn_clear_conversation.click(clear_conversation, [vlogger], [input_message, video_inp, chatbot, vlog_outp, state, vlogger, vlog_loaded]) vlog_btn.click(vlog_fn, [video_inp, vlogger, api_key_submitted], [vlog_outp, vlog_loaded, vlogger]) openai_api_key.submit(submit_api_key_fn, [openai_api_key, vlogger], [vlog_outp, api_key_submitted, vlogger]) demo.load(queur=False) demo.queue(concurrency_count=5) demo.launch(height='800px')