pages/💁‍ Open_Assistant.py

##### `💁‍ Open_Assistant.py`
##### https://huggingface.co/spaces/olivierdehaene/chat-llm-streaming/blob/main/README.md
##### https://huggingface.co/OpenAssistant/oasst-sft-1-pythia-12b
##### Please reach out to [email protected] for any questions
#### Loading needed Python libraries
import streamlit as st
import os
from text_generation import Client, InferenceAPIClient
from text_generation import InferenceAPIClient


st.header('💁‍ Open Assistant LLM')
st.write('This is the first iteration English supervised-fine-tuning (SFT) model of the Open-Assistant project. It is based on a Pythia 12B that was fine-tuned on ~22k human demonstrations of assistant conversations collected through the https://open-assistant.io/ human feedback web app before March 7, 2023.')
st.write('Question:green[Why is the sky blue?]')
client = InferenceAPIClient("OpenAssistant/oasst-sft-1-pythia-12b")
text = client.generate("<|prompter|>Why is the sky blue?<|endoftext|><|assistant|>").generated_text
st.write('Answer:green[' + str(text) + ']')

# Token Streaming
#text = ""
#for response in client.generate_stream("<|prompter|>Why is the sky blue?<|endoftext|><|assistant|>"):
#   if not response.token.special:
#       print(response.token.text)
#       text += response.token.text
#st.write(text)

#
# 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.\n"
# )
#
#
# def get_client(model: str):
#     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 == "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
#
#
# 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">🔥Large Language Model API 🚀Streaming🚀</h1>"""
# description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
# ```
# User: <utterance>
# Assistant: <utterance>
# User: <utterance>
# Assistant: <utterance>
# ...
# ```
# In this app, you can explore the outputs of multiple LLMs when prompted in this way.
# """
#
# 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.Column(elem_id = "col_container"):
#         model = gr.Radio(
#             value = "OpenAssistant/oasst-sft-1-pythia-12b",
#             choices = [
#                 "OpenAssistant/oasst-sft-1-pythia-12b",
#                 # "togethercomputer/GPT-NeoXT-Chat-Base-20B",
#                 "google/flan-t5-xxl",
#                 "google/flan-ul2",
#                 "bigscience/bloom",
#                 "bigscience/bloomz",
#                 "EleutherAI/gpt-neox-20b",
#             ],
#             label = "Model",
#             interactive = True,
#         )
#
#         chatbot = gr.Chatbot(elem_id = "chatbot")
#         inputs = gr.Textbox(
#             placeholder = "Hi there!", label = "Type an input and press Enter"
#         )
#         disclaimer = gr.Markdown(openchat_disclaimer, visible = False)
#         state = gr.State([])
#         b1 = gr.Button()
#
#         with gr.Accordion("Parameters", open = False):
#             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],
#     )
#     b1.click(
#         predict,
#         [
#             model,
#             inputs,
#             typical_p,
#             top_p,
#             temperature,
#             top_k,
#             repetition_penalty,
#             watermark,
#             chatbot,
#             state,
#         ],
#         [chatbot, state],
#     )
#     b1.click(reset_textbox, [], [inputs])
#     inputs.submit(reset_textbox, [], [inputs])
#
#     gr.Markdown(description)
#     demo.queue(concurrency_count = 16).launch(debug = True)