Hugging Face Workbench

openAssist.py

import gradio as gr
from transformers import pipeline
import time
import requests

model = "gpt2"
generator = pipeline("text-generation", model=model)
generator.model.config.temperature = 0.6
generator.model.config.repetition_penalty = 1.5
generator.model.config.length_penalty = 2
generator.model.config.max_length = 500
model_info = generator.model.config

def add_text(history, text):
    history = history + [(text, None)]
    return history, ""

def add_file(history, file):
    history = history + [((file.name,), None)]
    return history

def bot(history):
    # Get the last user input from the history
    user_input = history[-1][0]

    # Generate a response using the Hugging Face model
    response = generator(user_input, do_sample=True)[0]["generated_text"]
    
    # history[-1][1] = response
    history[-1][1] = ""
    for character in response:
            history[-1][1] += character
            time.sleep(0.05)
            yield history

with gr.Blocks() as demo:
    gr.Markdown("""# """ + model.upper())
    chatbot = gr.Chatbot([], elem_id="chatbot").style(height=750)

    with gr.Row():
        with gr.Column(scale=0.85):
            txt = gr.Textbox(
                show_label=False,
                placeholder="Enter text and press enter, or upload an image",
            ).style(container=False)
        with gr.Column(scale=0.15, min_width=0):
            btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])

    txt.submit(add_text, [chatbot, txt], [chatbot, txt]).then(
        bot, chatbot, chatbot
    )
    btn.upload(add_file, [chatbot, btn], [chatbot]).then(
        bot, chatbot, chatbot
    )
    gr.Markdown(
        """## Parameters\n<br><table><tr><th>Name</th><th>Value</th></tr>""" +
        "".join(f"<tr><td><b>{key}</b></td><td>{value}</td></tr>" for key, value in model_info.__dict__.items()) +
        """</table>"""
    )
    
demo.queue()
demo.launch(share=True)