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import os
import requests
requests.adapters.DEFAULT_TIMEOUT = 60
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
hf_api_key = os.environ['HF_API_KEY']
# Helper function
import requests, json
from text_generation import Client
#FalcomLM-instruct endpoint on the text_generation library
URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
client = Client(URL, timeout=120)
#Back to Lesson 2, time flies!
import gradio as gr
def generate(input, slider):
output = client.generate(input, max_new_tokens=slider).generated_text
return output
def format_chat_prompt(message, chat_history, instruction):
prompt = f"System:{instruction}"
for turn in chat_history:
user_message, bot_message = turn
prompt = f"{prompt}\nUser: {user_message}\nAssistant: {bot_message}"
prompt = f"{prompt}\nUser: {message}\nAssistant:"
return prompt
def respond(message, chat_history, instruction, temperature=0.7):
prompt = format_chat_prompt(message, chat_history, instruction)
chat_history = chat_history + [[message, ""]]
stream = client.generate_stream(prompt,
max_new_tokens=1024,
stop_sequences=["\nUser:", "<|endoftext|>"],
temperature=temperature)
#stop_sequences to not generate the user answer
acc_text = ""
#Streaming the tokens
for idx, response in enumerate(stream):
text_token = response.token.text
if response.details:
return
if idx == 0 and text_token.startswith(" "):
text_token = text_token[1:]
acc_text += text_token
last_turn = list(chat_history.pop(-1))
last_turn[-1] += acc_text
chat_history = chat_history + [last_turn]
yield "", chat_history
acc_text = ""
def loadGUI():
with gr.Blocks() as demo:
chatbot = gr.Chatbot(height=240) #just to fit the notebook
msg = gr.Textbox(label="Prompt")
with gr.Accordion(label="Advanced options",open=False):
system = gr.Textbox(label="System message", lines=2, value="A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.")
temperature = gr.Slider(label="temperature", minimum=0.1, maximum=1, value=0.7, step=0.1)
btn = gr.Button("Submit")
clear = gr.ClearButton(components=[msg, chatbot], value="Clear console")
btn.click(respond, inputs=[msg, chatbot, system], outputs=[msg, chatbot])
msg.submit(respond, inputs=[msg, chatbot, system], outputs=[msg, chatbot]) #Press enter to submit
gr.close_all()
demo.queue().launch(share=True)
def main():
loadGUI()
if __name__ == "__main__":
main()
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