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| import gradio as gr | |
| import torch | |
| import tensorflow as tf | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer, GPT2LMHeadModel, GPT2Tokenizer | |
| import time | |
| import numpy as np | |
| from torch.nn import functional as F | |
| import os | |
| from threading import Thread | |
| print(f"Starting to load the model to memory") | |
| tok = GPT2Tokenizer.from_pretrained("ethzanalytics/ai-msgbot-gpt2-XL-dialogue") | |
| m = GPT2LMHeadModel.from_pretrained("ethzanalytics/ai-msgbot-gpt2-XL-dialogue", pad_token_id=tok.eos_token_id) | |
| generator = pipeline('text-generation', model=m, tokenizer=tok) | |
| print(f"Sucessfully loaded the model to the memory") | |
| start_message = """You are an AI called assistant.""" | |
| class StopOnTokens(StoppingCriteria): | |
| def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: | |
| stop_ids = [50278, 50279, 50277, 1, 0] | |
| for stop_id in stop_ids: | |
| if input_ids[0][-1] == stop_id: | |
| return True | |
| return False | |
| def user(message, history): | |
| # Append the user's message to the conversation history | |
| return "", history + [[message, ""]] | |
| def chat(curr_system_message, history): | |
| # Initialize a StopOnTokens object | |
| stop = StopOnTokens() | |
| # Construct the input message string for the model by concatenating the current system message and conversation history | |
| messages = curr_system_message + \ | |
| "".join(["".join(["\nperson alpha:"+item[0], "\nperson beta:"+item[1]]) | |
| for item in history]) | |
| # Tokenize the messages string | |
| model_inputs = tok([messages], return_tensors="pt") | |
| streamer = TextIteratorStreamer( | |
| tok, skip_prompt=True, skip_special_tokens=True) | |
| generate_kwargs = dict( | |
| model_inputs, | |
| streamer=streamer, | |
| max_new_tokens=1024, | |
| do_sample=True, | |
| top_p=0.95, | |
| top_k=1000, | |
| temperature=1.0, | |
| num_beams=1, | |
| stopping_criteria=StoppingCriteriaList([stop]) | |
| ) | |
| t = Thread(target=m.generate, kwargs=generate_kwargs) | |
| t.start() | |
| # print(history) | |
| # Initialize an empty string to store the generated text | |
| partial_text = "" | |
| for new_text in streamer: | |
| # print(new_text) | |
| partial_text += new_text | |
| history[-1][1] = partial_text | |
| # Yield an empty string to cleanup the message textbox and the updated conversation history | |
| yield history | |
| return partial_text | |
| with gr.Blocks() as demo: | |
| # history = gr.State([]) | |
| gr.Markdown("## StableLM-Tuned-Alpha-7b Chat") | |
| gr.HTML('''<center><a href="https://huggingface.co/spaces/stabilityai/stablelm-tuned-alpha-chat?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space to skip the queue and run in a private space</center>''') | |
| chatbot = gr.Chatbot().style(height=500) | |
| with gr.Row(): | |
| with gr.Column(): | |
| msg = gr.Textbox(label="Chat Message Box", placeholder="Chat Message Box", | |
| show_label=False).style(container=False) | |
| with gr.Column(): | |
| with gr.Row(): | |
| submit = gr.Button("Submit") | |
| stop = gr.Button("Stop") | |
| clear = gr.Button("Clear") | |
| system_msg = gr.Textbox( | |
| start_message, label="System Message", interactive=False, visible=False) | |
| submit_event = msg.submit(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then( | |
| fn=chat, inputs=[system_msg, chatbot], outputs=[chatbot], queue=True) | |
| submit_click_event = submit.click(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then( | |
| fn=chat, inputs=[system_msg, chatbot], outputs=[chatbot], queue=True) | |
| stop.click(fn=None, inputs=None, outputs=None, cancels=[ | |
| submit_event, submit_click_event], queue=False) | |
| clear.click(lambda: None, None, [chatbot], queue=False) | |
| demo.queue(max_size=32, concurrency_count=2) | |
| demo.launch() | |