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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 | |