Spaces:
Paused
Paused
File size: 6,531 Bytes
819c0e1 046b1e3 141a5cd 3777a20 141a5cd 3777a20 141a5cd 046b1e3 141a5cd 046b1e3 141a5cd 8e1a142 141a5cd 046b1e3 ed48478 141a5cd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 |
import gradio as gr
from vid2persona import init
from vid2persona.pipeline import vlm
from vid2persona.pipeline import llm
init.auth_gcp()
init.get_env_vars()
prompt_tpl_path = "vid2persona/prompts"
async def extract_traits(video_path):
traits = await vlm.get_traits(
init.gcp_project_id,
init.gcp_project_location,
video_path,
prompt_tpl_path
)
if 'characters' in traits:
traits = traits['characters'][0]
return [
traits, [],
gr.Textbox("", interactive=True),
gr.Button(interactive=True),
gr.Button(interactive=True),
gr.Button(interactive=True)
]
async def conversation(
message: str, messages: list, traits: dict,
model_id: str, max_input_token_length: int,
max_new_tokens: int, temperature: float,
top_p: float, top_k: float, repetition_penalty: float,
):
messages = messages + [[message, ""]]
yield [messages, message, gr.Button(interactive=False), gr.Button(interactive=False)]
async for partial_response in llm.chat(
message, messages, traits,
prompt_tpl_path, model_id,
max_input_token_length, max_new_tokens,
temperature, top_p, top_k,
repetition_penalty, hf_token=init.hf_access_token
):
last_message = messages[-1]
last_message[1] = last_message[1] + partial_response
messages[-1] = last_message
yield [messages, "", gr.Button(interactive=False), gr.Button(interactive=False)]
yield [messages, "", gr.Button(interactive=True), gr.Button(interactive=True)]
async def regen_conversation(
messages: list, traits: dict,
model_id: str, max_input_token_length: int,
max_new_tokens: int, temperature: float,
top_p: float, top_k: float, repetition_penalty: float,
):
if len(messages) > 0:
message = messages[-1][0]
messages = messages[:-1]
messages = messages + [[message, ""]]
yield [messages, "", gr.Button(interactive=False), gr.Button(interactive=False)]
async for partial_response in llm.chat(
message, messages, traits,
prompt_tpl_path, model_id,
max_input_token_length, max_new_tokens,
temperature, top_p, top_k,
repetition_penalty, hf_token=init.hf_access_token
):
last_message = messages[-1]
last_message[1] = last_message[1] + partial_response
messages[-1] = last_message
yield [messages, "", gr.Button(interactive=False), gr.Button(interactive=False)]
yield [messages, "", gr.Button(interactive=True), gr.Button(interactive=True)]
with gr.Blocks(css="styles.css", theme=gr.themes.Soft()) as demo:
gr.Markdown("Vid2Persona", elem_classes=["md-center", "h1-font"])
gr.Markdown("This project breathes life into video characters by using AI to describe their personality and then chat with you as them.")
with gr.Column(elem_classes=["group"]):
with gr.Row():
video = gr.Video(label="upload short video clip")
traits = gr.Json(label="extracted traits")
with gr.Row():
trait_gen = gr.Button("generate traits")
with gr.Column(elem_classes=["group"]):
chatbot = gr.Chatbot([], label="chatbot", elem_id="chatbot", elem_classes=["chatbot-no-label"])
with gr.Row():
clear = gr.Button("clear conversation", interactive=False)
regen = gr.Button("regenerate the last", interactive=False)
stop = gr.Button("stop", interactive=False)
user_input = gr.Textbox(placeholder="ask anything", interactive=False, elem_classes=["textbox-no-label", "textbox-no-top-bottom-borders"])
with gr.Accordion("parameters' control pane", open=False):
model_id = gr.Dropdown(choices=init.ALLOWED_LLM_FOR_HF_PRO_ACCOUNTS, value="HuggingFaceH4/zephyr-7b-beta", label="Model ID")
with gr.Row():
max_input_token_length = gr.Slider(minimum=1024, maximum=4096, value=4096, label="max-input-tokens")
max_new_tokens = gr.Slider(minimum=128, maximum=2048, value=256, label="max-new-tokens")
with gr.Row():
temperature = gr.Slider(minimum=0, maximum=2, step=0.1, value=0.6, label="temperature")
top_p = gr.Slider(minimum=0, maximum=2, step=0.1, value=0.9, label="top-p")
top_k = gr.Slider(minimum=0, maximum=2, step=0.1, value=50, label="top-k")
repetition_penalty = gr.Slider(minimum=0, maximum=2, step=0.1, value=1.2, label="repetition-penalty")
with gr.Row():
gr.Markdown(
"[](https://github.com/deep-diver/Vid2Persona) "
"[](https://twitter.com/algo_diver) "
"[](https://twitter.com/RisingSayak )",
elem_id="bottom-md"
)
trait_gen.click(
extract_traits,
[video],
[traits, chatbot, user_input, clear, regen, stop]
)
conv = user_input.submit(
conversation,
[
user_input, chatbot, traits,
model_id, max_input_token_length,
max_new_tokens, temperature,
top_p, top_k, repetition_penalty,
],
[chatbot, user_input, clear, regen]
)
clear.click(
lambda: [
gr.Chatbot([]),
gr.Button(interactive=False),
gr.Button(interactive=False),
],
None, [chatbot, clear, regen]
)
conv_regen = regen.click(
regen_conversation,
[
chatbot, traits,
model_id, max_input_token_length,
max_new_tokens, temperature,
top_p, top_k, repetition_penalty,
],
[chatbot, user_input, clear, regen]
)
stop.click(
None, None, None,
cancels=[conv, conv_regen]
)
gr.Examples(
[["assets/sample1.mp4"], ["assets/sample2.mp4"], ["assets/sample3.mp4"], ["assets/sample4.mp4"]]
video,
[traits, chatbot, user_input, clear, regen, stop],
extract_traits,
cache_examples=True
)
demo.launch() |