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from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import pipeline
import gradio as gr
import time
REPO_ID = "sayanbanerjee32/ms-phi2-qlora-oasst1"
model = AutoModelForCausalLM.from_pretrained(REPO_ID)
tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
def generate_text(prompt, chat_history, num_new_tokens = 100):
# prompt = "<|prompter|>What is 2 + 2?<|endoftext|><|assistant|>" # change to your desired prompt
input_prompt = ''
if len(chat_history) > 0:
input_prompt += "<|prompter|>" + chat_history[-1][0] + "<|endoftext|><|assistant|>" + chat_history[-1][1] + "<|endoftext|>"
input_prompt += "<|prompter|>" + prompt + "<|endoftext|><|assistant|>"
# Count the number of tokens in the prompt
num_prompt_tokens = len(tokenizer(input_prompt)['input_ids'])
# Calculate the maximum length for the generation
max_length = num_prompt_tokens + num_new_tokens
gen = pipeline('text-generation', model=model,
tokenizer=tokenizer, max_length= max_length )
result = gen(prompt)
return result[0]['generated_text'].replace(prompt, '')
with gr.Blocks() as demo:
gr.HTML("<h1 align = 'center'> AskMe anything simple </h1>")
gr.HTML("<h4 align = 'center'> ChatBot powered by Microsoft-Phi-2 finetuned on OpenAssistant dataset</h4>")
chatbot = gr.Chatbot()
msg = gr.Textbox()
gr.Examples(["What do you think about ChatGPT?",
"How would the Future of AI in 10 Years look?",
"Write a announcement tweet for medium.com readers about the new blogpost on 'Open Assistant is open source ChatGPT that you don\'t wanna miss out'",
"Please implement the Timsort algorithm on Lean 4 and explain your code",
"How do I build a PC?"],
inputs = msg)
clear = gr.ClearButton([msg, chatbot])
def respond(message, chat_history):
bot_message = generate_text(message, chat_history)
chat_history.append((message, bot_message))
time.sleep(2)
return "", chat_history
msg.submit(respond, [msg, chatbot], [msg, chatbot])
# # for collab
# demo.launch(debug=True)
if __name__ == '__main__':
demo.launch()
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