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import gradio as gr
from huggingface_hub import InferenceClient
import random
import textwrap
# Define the model to be used
model = "mistralai/Mixtral-8x7B-Instruct-v0.1"
client = InferenceClient(model)
# Embedded system prompt
system_prompt_text = "You are a smart and helpful co-worker of Thailand based multi-national company PTT, and PTTEP. You help with any kind of request and provide a detailed answer to the question. But if you are asked about something unethical or dangerous, you must refuse and provide a safe and respectful way to handle that"
# Read the content of the info.md file
with open("info.md", "r") as file:
info_md_content = file.read()
# Chunk the info.md content into smaller sections
chunk_size = 2000 # Adjust this size as needed
info_md_chunks = textwrap.wrap(info_md_content, chunk_size)
def get_relevant_chunk(prompt, chunks):
# For simplicity, we just use the first chunk. You can improve this by adding more sophisticated logic.
return chunks[0]
def format_prompt_mixtral(message, history, info_md_content):
prompt = "<s>"
relevant_chunk = get_relevant_chunk(message, info_md_content)
prompt += f"{relevant_chunk}\n\n" # Add the relevant chunk of info.md at the beginning
prompt += f"{system_prompt_text}\n\n" # Add the system prompt
if history:
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def chat_inf(prompt, history, seed, temp, tokens, top_p, rep_p):
generate_kwargs = dict(
temperature=temp,
max_new_tokens=tokens,
top_p=top_p,
repetition_penalty=rep_p,
do_sample=True,
seed=seed,
)
formatted_prompt = format_prompt_mixtral(prompt, history, info_md_chunks)
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
yield [(prompt, output)]
history.append((prompt, output))
yield history
def clear_fn():
return None, None
rand_val = random.randint(1, 1111111111111111)
def check_rand(inp, val):
if inp:
return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1, 1111111111111111))
else:
return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=int(val))
with gr.Blocks() as app: # Add auth here
gr.HTML("""<center><h1 style='font-size:xx-large;'>PTT Chatbot</h1><br><h3>running on Huggingface Inference </h3><br><h7>EXPERIMENTAL""")
with gr.Row():
chat = gr.Chatbot(height=500)
with gr.Group():
with gr.Row():
with gr.Column(scale=3):
inp = gr.Textbox(label="Prompt", lines=5, interactive=True) # Increased lines and interactive
with gr.Row():
with gr.Column(scale=2):
btn = gr.Button("Chat")
with gr.Column(scale=1):
with gr.Group():
stop_btn = gr.Button("Stop")
clear_btn = gr.Button("Clear")
with gr.Column(scale=1):
with gr.Group():
rand = gr.Checkbox(label="Random Seed", value=True)
seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val)
tokens = gr.Slider(label="Max new tokens", value=3840, minimum=0, maximum=8000, step=64, interactive=True, visible=True, info="The maximum number of tokens")
temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum=2.0, value=1.0)
hid1 = gr.Number(value=1, visible=False)
go = btn.click(check_rand, [rand, seed], seed).then(chat_inf, [inp, chat, seed, temp, tokens, top_p, rep_p], chat)
stop_btn.click(None, None, None, cancels=[go])
clear_btn.click(clear_fn, None, [inp, chat])
app.queue(default_concurrency_limit=10).launch(auth=("admin", "0112358"))
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