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import os
import math
import gradio as gr
def make_chatbots(output_label0, output_label0_model2, **kwargs):
text_outputs = []
chat_kwargs = []
for model_state_lock in kwargs['model_states']:
if os.environ.get('DEBUG_MODEL_LOCK'):
model_name = model_state_lock["base_model"] + " : " + model_state_lock["inference_server"]
else:
model_name = model_state_lock["base_model"]
output_label = f'h2oGPT [{model_name}]'
min_width = 250 if kwargs['gradio_size'] in ['small', 'large', 'medium'] else 160
chat_kwargs.append(dict(label=output_label, visible=kwargs['model_lock'], elem_classes='chatsmall',
height=kwargs['height'] or 400, min_width=min_width))
if kwargs['model_lock_columns'] == -1:
kwargs['model_lock_columns'] = len(kwargs['model_states'])
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(kwargs['model_states']) / 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 == 2:
with gr.Row():
for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
if mii >= len(kwargs['model_states']) / 2:
continue
text_outputs.append(gr.Chatbot(**chat_kwargs1))
with gr.Row():
for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
if mii < len(kwargs['model_states']) / 2:
continue
text_outputs.append(gr.Chatbot(**chat_kwargs1))
elif nrows == 3:
with gr.Row():
for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
if mii >= 1 * len(kwargs['model_states']) / 3:
continue
text_outputs.append(gr.Chatbot(**chat_kwargs1))
with gr.Row():
for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
if mii < 1 * len(kwargs['model_states']) / 3 or mii >= 2 * len(kwargs['model_states']) / 3:
continue
text_outputs.append(gr.Chatbot(**chat_kwargs1))
with gr.Row():
for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
if mii < 2 * len(kwargs['model_states']) / 3:
continue
text_outputs.append(gr.Chatbot(**chat_kwargs1))
elif nrows >= 4:
with gr.Row():
for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
if mii >= 1 * len(kwargs['model_states']) / 4:
continue
text_outputs.append(gr.Chatbot(**chat_kwargs1))
with gr.Row():
for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
if mii < 1 * len(kwargs['model_states']) / 4 or mii >= 2 * len(kwargs['model_states']) / 4:
continue
text_outputs.append(gr.Chatbot(**chat_kwargs1))
with gr.Row():
for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
if mii < 2 * len(kwargs['model_states']) / 4 or mii >= 3 * len(kwargs['model_states']) / 4:
continue
text_outputs.append(gr.Chatbot(**chat_kwargs1))
with gr.Row():
for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
if mii < 3 * len(kwargs['model_states']) / 4:
continue
text_outputs.append(gr.Chatbot(**chat_kwargs1))
with gr.Row():
text_output = gr.Chatbot(label=output_label0, visible=not kwargs['model_lock'], height=kwargs['height'] or 400)
text_output2 = gr.Chatbot(label=output_label0_model2,
visible=False and not kwargs['model_lock'], height=kwargs['height'] or 400)
return text_output, text_output2, text_outputs
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