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# gradio is a UI library for machine learning models
import gradio as gr
# loguru is a library for logging
from loguru import logger
# generative pre-trained transformer model
from model.model import Model
# load model
model = Model()
# These functions are responsible for defining the chatbot's behavior
# when the user interacts with the interface. The respond function
# receives a question and a conversation history. It defines the
# question in the model (model.question) and calls the
# question_answerer method to get the answer. The response
# is added to the history and returned as a result.
def respond(question, history):
model.question = question
history.append((question, model.question_answerer()))
return "", history
# The set_context function takes a context and sets that context in
# the model (model.context).
def set_context(context):
model.context = context
# In this part, the Gradio interface is created.
# the interface has two tabs: "Chat" and "Context".
with gr.Blocks() as interface:
# In the "Chat" tab, there is a Chatbot component which is
# used to display the chatbot conversation. There is also
# a Textbox component called prompt_gradio_component
# used to receive the question from the user. The
# generate_gradio_component button is responsible
# for calling the respond function when clicked.
# The clear_gradio_component button is used to
# clear input fields and conversation.
with gr.Tab("Chat"):
chatbot_gradio_component = gr.Chatbot(label="My Own Chatbot")
prompt_gradio_component = gr.Textbox(label="Prompt", lines=2)
generate_gradio_component = gr.Button("Generate")
clear_gradio_component = gr.ClearButton([prompt_gradio_component, chatbot_gradio_component])
generate_gradio_component.click(respond, [prompt_gradio_component, chatbot_gradio_component], [prompt_gradio_component, chatbot_gradio_component])
# In the "Context" tab, there is a Textbox component called
# context_gradio_component used to receive the chatbot
# context. The set_context_gradio_component button is
# responsible for calling the set_context function
# when clicked. The clear_gradio_component button
# is used to clear the input field.
with gr.Tab("Context"):
context_gradio_component = gr.Textbox(label="Context", info="your context must be <= 512 tokens!", lines=10)
set_context_gradio_component = gr.Button("Set")
clear_gradio_component = gr.ClearButton([context_gradio_component])
set_context_gradio_component.click(set_context, [context_gradio_component])
# In this part, the interface is launched and executed. The launch()
# function is called to launch the Gradio interface.
# If any errors occur during runtime, they are
# caught and logged using the loguru library.
if __name__ == "__main__":
try:
interface.launch()
except Exception as error:
logger.error(error) |