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import os
import math
import gradio as gr
def get_chatbot_name(base_model, model_path_llama, inference_server='', debug=False):
if not debug:
inference_server = ''
else:
inference_server = ' : ' + inference_server
if base_model == 'llama':
model_path_llama = os.path.basename(model_path_llama)
if model_path_llama.endswith('?download=true'):
model_path_llama = model_path_llama.replace('?download=true', '')
return f'h2oGPT [Model: {model_path_llama}{inference_server}]'
else:
return f'h2oGPT [Model: {base_model}{inference_server}]'
def get_avatars(base_model, model_path_llama, inference_server=''):
if base_model == 'llama':
base_model = model_path_llama
if inference_server is None:
inference_server = ''
model_base = os.getenv('H2OGPT_MODEL_BASE', 'models/')
human_avatar = "human.jpg"
if 'h2ogpt-gm'.lower() in base_model.lower():
bot_avatar = "h2oai.png"
elif 'mistralai'.lower() in base_model.lower() or \
'mistral'.lower() in base_model.lower() or \
'mixtral'.lower() in base_model.lower():
bot_avatar = "mistralai.png"
elif '01-ai/Yi-'.lower() in base_model.lower():
bot_avatar = "yi.svg"
elif 'wizard' in base_model.lower():
bot_avatar = "wizard.jpg"
elif 'openchat' in base_model.lower():
bot_avatar = "openchat.png"
elif 'vicuna' in base_model.lower():
bot_avatar = "vicuna.jpeg"
elif 'longalpaca' in base_model.lower():
bot_avatar = "longalpaca.png"
elif 'llama2-70b-chat' in base_model.lower():
bot_avatar = "meta.png"
elif 'llama2-13b-chat' in base_model.lower():
bot_avatar = "meta.png"
elif 'llama2-7b-chat' in base_model.lower():
bot_avatar = "meta.png"
elif 'llama2' in base_model.lower():
bot_avatar = "lama2.jpeg"
elif 'llama-2' in base_model.lower():
bot_avatar = "lama2.jpeg"
elif 'llama' in base_model.lower():
bot_avatar = "lama.jpeg"
elif 'openai' in base_model.lower() or 'openai' in inference_server.lower():
bot_avatar = "openai.png"
elif 'hugging' in base_model.lower():
bot_avatar = "hf-logo.png"
elif 'claude' in base_model.lower():
bot_avatar = "anthropic.jpeg"
elif 'gemini' in base_model.lower():
bot_avatar = "google.png"
else:
bot_avatar = "h2oai.png"
bot_avatar = os.path.join(model_base, bot_avatar)
human_avatar = os.path.join(model_base, human_avatar)
human_avatar = human_avatar if os.path.isfile(human_avatar) else None
bot_avatar = bot_avatar if os.path.isfile(bot_avatar) else None
return human_avatar, bot_avatar
def make_chatbots(output_label0, output_label0_model2, **kwargs):
visible_models = kwargs['visible_models']
all_models = kwargs['all_possible_visible_models']
text_outputs = []
chat_kwargs = []
min_width = 250 if kwargs['gradio_size'] in ['small', 'large', 'medium'] else 160
for model_state_locki, model_state_lock in enumerate(kwargs['model_states']):
output_label = get_chatbot_name(model_state_lock["base_model"],
model_state_lock['llamacpp_dict']["model_path_llama"],
model_state_lock["inference_server"],
debug=bool(os.environ.get('DEBUG_MODEL_LOCK', 0)))
if kwargs['avatars']:
avatar_images = get_avatars(model_state_lock["base_model"],
model_state_lock['llamacpp_dict']["model_path_llama"],
model_state_lock["inference_server"])
else:
avatar_images = None
chat_kwargs.append(dict(render_markdown=kwargs.get('render_markdown', True),
label=output_label,
show_label=kwargs.get('visible_chatbot_label', True),
elem_classes='chatsmall',
height=kwargs['height'] or 400,
min_width=min_width,
avatar_images=avatar_images,
show_copy_button=kwargs['show_copy_button'],
visible=kwargs['model_lock'] and (visible_models is None or
model_state_locki in visible_models or
all_models[model_state_locki] in visible_models
)))
# base view on initial visible choice
if visible_models and kwargs['model_lock_layout_based_upon_initial_visible']:
len_visible = len(visible_models)
else:
len_visible = len(kwargs['model_states'])
if kwargs['model_lock_columns'] == -1:
kwargs['model_lock_columns'] = len_visible
if kwargs['model_lock_columns'] is None:
kwargs['model_lock_columns'] = 3
ncols = kwargs['model_lock_columns']
if kwargs['model_states'] == 0:
nrows = 0
else:
nrows = math.ceil(len_visible / kwargs['model_lock_columns'])
if kwargs['model_lock_columns'] == 0:
# not using model_lock
pass
elif nrows <= 1:
with gr.Row():
for chat_kwargs1, model_state_lock in zip(chat_kwargs, kwargs['model_states']):
text_outputs.append(gr.Chatbot(**chat_kwargs1))
elif nrows == kwargs['model_states']:
with gr.Row():
for chat_kwargs1, model_state_lock in zip(chat_kwargs, kwargs['model_states']):
text_outputs.append(gr.Chatbot(**chat_kwargs1))
elif nrows > 0:
len_chatbots = len(kwargs['model_states'])
nrows = math.ceil(len_chatbots / kwargs['model_lock_columns'])
for nrowi in range(nrows):
with gr.Row():
for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
if mii < nrowi * len_chatbots / nrows or mii >= (1 + nrowi) * len_chatbots / nrows:
continue
text_outputs.append(gr.Chatbot(**chat_kwargs1))
if len(kwargs['model_states']) > 0:
assert len(text_outputs) == len(kwargs['model_states'])
if kwargs['avatars']:
avatar_images = get_avatars(kwargs["base_model"], kwargs['llamacpp_dict']["model_path_llama"],
kwargs["inference_server"])
else:
avatar_images = None
no_model_lock_chat_kwargs = dict(render_markdown=kwargs.get('render_markdown', True),
show_label=kwargs.get('visible_chatbot_label', True),
elem_classes='chatsmall',
height=kwargs['height'] or 400,
min_width=min_width,
show_copy_button=kwargs['show_copy_button'],
avatar_images=avatar_images,
)
with gr.Row():
text_output = gr.Chatbot(label=output_label0,
visible=not kwargs['model_lock'],
**no_model_lock_chat_kwargs,
)
text_output2 = gr.Chatbot(label=output_label0_model2,
visible=False and not kwargs['model_lock'],
**no_model_lock_chat_kwargs)
return text_output, text_output2, text_outputs
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