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import gradio as gr
from task import tasks_config
from pipeline_utils import handle_task_change, review_training_choices, test_pipeline
from playground_utils import create_playground_header, create_playground_footer, create_tabs_header
playground = gr.Blocks()
with playground:
create_playground_header()
with gr.Tabs():
with gr.TabItem("Text"):
radio, test_pipeline_button = create_tabs_header()
with gr.Row(visible=True) as use_pipeline:
with gr.Column():
task_dropdown = gr.Dropdown(
choices=[(task["name"], task_id)
for task_id, task in tasks_config.items()],
label="Task",
interactive=True,
info="Select Pipelines for natural language processing tasks or type if you have your own."
)
model_dropdown = gr.Dropdown(
[], label="Model", info="Select appropriate Model based on the task you selected")
prompt_textarea = gr.TextArea(
label="Prompt",
value="Enter your prompt here",
text_align="left",
info="Copy/Paste or type your prompt to try out. Make sure to provide clear prompt or try with different prompts"
)
context_for_question_answer = gr.TextArea(
label="Context",
value="Enter Context for your question here",
visible=False,
interactive=True,
info="Question answering tasks return an answer given a question. If you’ve ever asked a virtual assistant like Alexa, Siri or Google what the weather is, then you’ve used a question answering model before. Here, we are doing Extractive(extract the answer from the given context) Question answering. "
)
task_dropdown.change(handle_task_change,
inputs=[task_dropdown],
outputs=[context_for_question_answer,
model_dropdown, task_dropdown])
with gr.Column():
text = gr.TextArea(label="Generated Text")
radio.change(review_training_choices,
inputs=radio, outputs=use_pipeline)
test_pipeline_button.click(test_pipeline,
inputs=[
task_dropdown, model_dropdown, prompt_textarea, context_for_question_answer],
outputs=text)
with gr.TabItem("Image"):
radio, test_pipeline_button = create_tabs_header()
gr.Markdown("""
> WIP
""")
with gr.TabItem("Audio"):
radio, test_pipeline_button = create_tabs_header()
gr.Markdown("""
> WIP
""")
create_playground_footer()
playground.launch()
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