app.py

import os

import gradio as gr
import retrieval
from text_generation import Client, InferenceAPIClient

# UNCOMMENT ONLY WHEN RUNNING LOCALLY (not on Spaces)
# from dotenv import load_dotenv
# load API keys from globally-availabe .env file
# SECRETS_FILEPATH = "/mnt/project/chatbotai/huggingface_cache/internal_api_keys.env"
# load_dotenv(dotenv_path=SECRETS_FILEPATH, override=True)

openchat_preprompt = (
    "\n<human>: Hi!\n<bot>: My name is Bot, model version is 0.15, part of an open-source kit for "
    "fine-tuning new bots! I was created by Together, LAION, and Ontocord.ai and the open-source "
    "community. I am not human, not evil and not alive, and thus have no thoughts and feelings, "
    "but I am programmed to be helpful, polite, honest, and friendly. I'm really smart at answering electrical engineering questions.\n")

# LOAD MODELS
ta = retrieval.Retrieval()
NUM_ANSWERS_GENERATED = 3


def clip_img_search(img):
  if img is None:
    return []
  else:
    return ta.reverse_img_search(img)


def get_client(model: str):
  if model == "Rallio67/joi2_20Be_instruct_alpha":
    return Client(os.getenv("JOI_API_URL"))
  if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
    return Client(os.getenv("OPENCHAT_API_URL"))
  return InferenceAPIClient(model, token=os.getenv("HF_TOKEN", None))


def get_usernames(model: str):
  """
    Returns:
        (str, str, str, str): pre-prompt, username, bot name, separator
    """
  if model == "OpenAssistant/oasst-sft-1-pythia-12b":
    return "", "<|prompter|>", "<|assistant|>", "<|endoftext|>"
  if model == "Rallio67/joi2_20Be_instruct_alpha":
    return "", "User: ", "Joi: ", "\n\n"
  if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
    return openchat_preprompt, "<human>: ", "<bot>: ", "\n"
  return "", "User: ", "Assistant: ", "\n"


def predict(
    model: str,
    inputs: str,
    typical_p: float,
    top_p: float,
    temperature: float,
    top_k: int,
    repetition_penalty: float,
    watermark: bool,
    chatbot,
    history,
):
  client = get_client(model)
  preprompt, user_name, assistant_name, sep = get_usernames(model)

  history.append(inputs)

  past = []
  for data in chatbot:
    user_data, model_data = data

    if not user_data.startswith(user_name):
      user_data = user_name + user_data
    if not model_data.startswith(sep + assistant_name):
      model_data = sep + assistant_name + model_data

    past.append(user_data + model_data.rstrip() + sep)

  if not inputs.startswith(user_name):
    inputs = user_name + inputs

  total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip()

  partial_words = ""

  if model == "OpenAssistant/oasst-sft-1-pythia-12b":
    iterator = client.generate_stream(
        total_inputs,
        typical_p=typical_p,
        truncate=1000,
        watermark=watermark,
        max_new_tokens=500,
    )
  else:
    iterator = client.generate_stream(
        total_inputs,
        top_p=top_p if top_p < 1.0 else None,
        top_k=top_k,
        truncate=1000,
        repetition_penalty=repetition_penalty,
        watermark=watermark,
        temperature=temperature,
        max_new_tokens=500,
        stop_sequences=[user_name.rstrip(), assistant_name.rstrip()],
    )

  for i, response in enumerate(iterator):
    if response.token.special:
      continue

    partial_words = partial_words + response.token.text
    if partial_words.endswith(user_name.rstrip()):
      partial_words = partial_words.rstrip(user_name.rstrip())
    if partial_words.endswith(assistant_name.rstrip()):
      partial_words = partial_words.rstrip(assistant_name.rstrip())

    if i == 0:
      history.append(" " + partial_words)
    elif response.token.text not in user_name:
      history[-1] = partial_words

    chat = [(history[i].strip(), history[i + 1].strip()) for i in range(0, len(history) - 1, 2)]
    yield chat, history, None, None, None, []

  # Pinecone context retrieval
  top_context_list = ta.retrieve_contexts_from_pinecone(user_question=inputs, topk=NUM_ANSWERS_GENERATED)
  yield chat, history, top_context_list[0], top_context_list[1], top_context_list[2], []

  # run CLIP
  images_list = ta.clip_text_to_image(inputs)
  yield chat, history, top_context_list[0], top_context_list[1], top_context_list[2], images_list


def reset_textbox():
  return gr.update(value="")


def radio_on_change(
    value: str,
    disclaimer,
    typical_p,
    top_p,
    top_k,
    temperature,
    repetition_penalty,
    watermark,
):
  if value == "OpenAssistant/oasst-sft-1-pythia-12b":
    typical_p = typical_p.update(value=0.2, visible=True)
    top_p = top_p.update(visible=False)
    top_k = top_k.update(visible=False)
    temperature = temperature.update(visible=False)
    disclaimer = disclaimer.update(visible=False)
    repetition_penalty = repetition_penalty.update(visible=False)
    watermark = watermark.update(False)
  elif value == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
    typical_p = typical_p.update(visible=False)
    top_p = top_p.update(value=0.25, visible=True)
    top_k = top_k.update(value=50, visible=True)
    temperature = temperature.update(value=0.6, visible=True)
    repetition_penalty = repetition_penalty.update(value=1.01, visible=True)
    watermark = watermark.update(False)
    disclaimer = disclaimer.update(visible=True)
  else:
    typical_p = typical_p.update(visible=False)
    top_p = top_p.update(value=0.95, visible=True)
    top_k = top_k.update(value=4, visible=True)
    temperature = temperature.update(value=0.5, visible=True)
    repetition_penalty = repetition_penalty.update(value=1.03, visible=True)
    watermark = watermark.update(True)
    disclaimer = disclaimer.update(visible=False)
  return (
      disclaimer,
      typical_p,
      top_p,
      top_k,
      temperature,
      repetition_penalty,
      watermark,
  )


title = """<h1 align="center">🔥Teaching Assistant Chatbot"""
description = """
"""

openchat_disclaimer = """
<div align="center">Checkout the official <a href=https://huggingface.co/spaces/togethercomputer/OpenChatKit>OpenChatKit feedback app</a> for the full experience.</div>
"""

with gr.Blocks(css="""#col_container {margin-left: auto; margin-right: auto;}
                #chatbot {height: 520px; overflow: auto;}""") as demo:
  gr.HTML(title)
  with gr.Row():
    with gr.Accordion("Model choices", open=False, visible=True):
      model = gr.Radio(
          value="OpenAssistant/oasst-sft-1-pythia-12b",
          choices=[
              "OpenAssistant/oasst-sft-1-pythia-12b",
              # "togethercomputer/GPT-NeoXT-Chat-Base-20B",
              "Rallio67/joi2_20Be_instruct_alpha",
              "google/flan-t5-xxl",
              "google/flan-ul2",
              "bigscience/bloom",
              "bigscience/bloomz",
              "EleutherAI/gpt-neox-20b",
          ],
          label="",
          interactive=True,
      )
  # with gr.Row():
  #     with gr.Column():
  #         use_gpt3_checkbox = gr.Checkbox(label="Include GPT-3 (paid)?")
  #     with gr.Column():
  #         use_equation_checkbox = gr.Checkbox(label="Prioritize equations?")
  state = gr.State([])

  with gr.Row():
    with gr.Column():
      chatbot = gr.Chatbot(elem_id="chatbot")
      inputs = gr.Textbox(placeholder="Ask an Electrical Engineering question!", label="Send a message...")
      examples = gr.Examples(
          examples=[
              "What is a Finite State Machine?",
              "How do you design a functional a Two-Bit Gray Code Counter?",
              "How can we compare an 8-bit 2's complement number to the value -1 using AND, OR, and NOT?",
              "What does the uninterrupted counting cycle label mean?",
          ],
          inputs=[inputs],
          outputs=[],
      )
  gr.Markdown("## Relevant Textbook Passages & Lecture Transcripts")
  with gr.Row():
    with gr.Column():
      context1 = gr.Textbox(label="Context 1")
    with gr.Column():
      context2 = gr.Textbox(label="Context 2")
    with gr.Column():
      context3 = gr.Textbox(label="Context 3")

  gr.Markdown("## Relevant Lecture Slides")
  with gr.Row():
    with gr.Column(scale=2.6):
      lec_gallery = gr.Gallery(label="Lecture images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
    with gr.Column(scale=1):
      inp_image = gr.Image(type="pil", label="Reverse Image Search (optional)", shape=(224, 398))

  inp_image.change(fn=clip_img_search, inputs=inp_image, outputs=lec_gallery, scroll_to_output=True)
  disclaimer = gr.Markdown(openchat_disclaimer, visible=False)
  # state = gr.State([])

  with gr.Row():
    with gr.Accordion("Parameters", open=False, visible=True):
      typical_p = gr.Slider(
          minimum=-0,
          maximum=1.0,
          value=0.2,
          step=0.05,
          interactive=True,
          label="Typical P mass",
      )
      top_p = gr.Slider(
          minimum=-0,
          maximum=1.0,
          value=0.25,
          step=0.05,
          interactive=True,
          label="Top-p (nucleus sampling)",
          visible=False,
      )
      temperature = gr.Slider(
          minimum=-0,
          maximum=5.0,
          value=0.6,
          step=0.1,
          interactive=True,
          label="Temperature",
          visible=False,
      )
      top_k = gr.Slider(
          minimum=1,
          maximum=50,
          value=50,
          step=1,
          interactive=True,
          label="Top-k",
          visible=False,
      )
      repetition_penalty = gr.Slider(
          minimum=0.1,
          maximum=3.0,
          value=1.03,
          step=0.01,
          interactive=True,
          label="Repetition Penalty",
          visible=False,
      )
      watermark = gr.Checkbox(value=False, label="Text watermarking")

  model.change(
      lambda value: radio_on_change(
          value,
          disclaimer,
          typical_p,
          top_p,
          top_k,
          temperature,
          repetition_penalty,
          watermark,
      ),
      inputs=model,
      outputs=[
          disclaimer,
          typical_p,
          top_p,
          top_k,
          temperature,
          repetition_penalty,
          watermark,
      ],
  )

  inputs.submit(
      predict,
      [
          model,
          inputs,
          typical_p,
          top_p,
          temperature,
          top_k,
          repetition_penalty,
          watermark,
          chatbot,
          state,
      ],
      [chatbot, state, context1, context2, context3, lec_gallery],
  )
  inputs.submit(reset_textbox, [], [inputs])

  gr.Markdown(description)
  demo.queue(concurrency_count=16).launch(debug=True)