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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) |