from typing import Iterator import gradio as gr import torch from model import get_input_token_length, run DEFAULT_SYSTEM_PROMPT = """\ You are a software engineer reporting to a senior software engineer. Reply with highest quality, PhD level, detailed, logical, precise, clean answers. """ MAX_MAX_NEW_TOKENS = 2048 DEFAULT_MAX_NEW_TOKENS = 1024 MAX_INPUT_TOKEN_LENGTH = 4000 DESCRIPTION = """ """ LICENSE = """

--- """ if not torch.cuda.is_available(): DESCRIPTION += '\n

Running on CPU.

' def clear_and_save_textbox(message: str) -> tuple[str, str]: return '', message def display_input(message: str, history: list[tuple[str, str]]) -> list[tuple[str, str]]: history.append((message, '')) return history def delete_prev_fn( history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]: try: message, _ = history.pop() except IndexError: message = '' return history, message or '' def generate( message: str, history_with_input: list[tuple[str, str]], system_prompt: str, max_new_tokens: int, temperature: float, top_p: float, top_k: int, ) -> Iterator[list[tuple[str, str]]]: if max_new_tokens > MAX_MAX_NEW_TOKENS: raise ValueError history = history_with_input[:-1] generator = run(message, history, system_prompt, max_new_tokens, temperature, top_p, top_k) try: first_response = next(generator) yield history + [(message, first_response)] except StopIteration: yield history + [(message, '')] for response in generator: yield history + [(message, response)] def process_example(message: str) -> tuple[str, list[tuple[str, str]]]: generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 1024, 1, 0.95, 50) for x in generator: pass return '', x def check_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> None: input_token_length = get_input_token_length(message, chat_history, system_prompt) if input_token_length > MAX_INPUT_TOKEN_LENGTH: raise gr.Error(f'The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again.') with gr.Blocks(css='style.css') as demo: gr.Header("GalaxiCode.ai", level=1, font_size=24) with gr.Group(): chatbot = gr.Chatbot(label='Chatbot') with gr.Row(): textbox = gr.Textbox( container=False, show_label=False, placeholder='Type a message...', scale=10, ) submit_button = gr.Button('Submit', variant='primary', scale=1, min_width=0) with gr.Row(): retry_button = gr.Button('🔄 Retry', variant='secondary') undo_button = gr.Button('↩ī¸ Undo', variant='secondary') clear_button = gr.Button('🗑ī¸ Clear', variant='secondary') saved_input = gr.State() with gr.Accordion(label='Advanced options', open=False): system_prompt = gr.Textbox(label='System prompt', value=DEFAULT_SYSTEM_PROMPT, lines=6) max_new_tokens = gr.Slider( label='Max new tokens', minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS, ) temperature = gr.Slider( label='Temperature', minimum=0.1, maximum=4.0, step=0.1, value=1.0, ) top_p = gr.Slider( label='Top-p (nucleus sampling)', minimum=0.05, maximum=1.0, step=0.05, value=0.95, ) top_k = gr.Slider( label='Top-k', minimum=1, maximum=1000, step=1, value=50, ) gr.Examples( examples=[ "X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.1)\n\n# Train a logistic regression model, predict the labels on the test set and compute the accuracy score", "// Returns every other value in the array as a new array.\nfunction everyOther(arr) {", "Poor English: She no went to the market. Corrected English:", "def alternating(list1, list2):\n results = []\n for i in range(min(len(list1), len(list2))):\n results.append(list1[i])\n results.append(list2[i])\n if len(list1) > len(list2):\n \n else:\n results.extend(list2[i+1:])\n return results", "def remove_non_ascii(s: str) -> str:\n \"\"\" \nprint(remove_non_ascii('afkdj$$('))", ], inputs=textbox, outputs=[textbox, chatbot], fn=process_example, cache_examples=True, ) gr.Markdown(LICENSE) textbox.submit( fn=clear_and_save_textbox, inputs=textbox, outputs=[textbox, saved_input], api_name=False, queue=False, ).then( fn=display_input, inputs=[saved_input, chatbot], outputs=chatbot, api_name=False, queue=False, ).then( fn=check_input_token_length, inputs=[saved_input, chatbot, system_prompt], api_name=False, queue=False, ).success( fn=generate, inputs=[ saved_input, chatbot, system_prompt, max_new_tokens, temperature, top_p, top_k, ], outputs=chatbot, api_name=False, ) button_event_preprocess = submit_button.click( fn=clear_and_save_textbox, inputs=textbox, outputs=[textbox, saved_input], api_name=False, queue=False, ).then( fn=display_input, inputs=[saved_input, chatbot], outputs=chatbot, api_name=False, queue=False, ).then( fn=check_input_token_length, inputs=[saved_input, chatbot, system_prompt], api_name=False, queue=False, ).success( fn=generate, inputs=[ saved_input, chatbot, system_prompt, max_new_tokens, temperature, top_p, top_k, ], outputs=chatbot, api_name=False, ) retry_button.click( fn=delete_prev_fn, inputs=chatbot, outputs=[chatbot, saved_input], api_name=False, queue=False, ).then( fn=display_input, inputs=[saved_input, chatbot], outputs=chatbot, api_name=False, queue=False, ).then( fn=generate, inputs=[ saved_input, chatbot, system_prompt, max_new_tokens, temperature, top_p, top_k, ], outputs=chatbot, api_name=False, ) undo_button.click( fn=delete_prev_fn, inputs=chatbot, outputs=[chatbot, saved_input], api_name=False, queue=False, ).then( fn=lambda x: x, inputs=[saved_input], outputs=textbox, api_name=False, queue=False, ) clear_button.click( fn=lambda: ([], ''), outputs=[chatbot, saved_input], queue=False, api_name=False, ) demo.queue(max_size=20).launch()