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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 Health consultant and therapist named CareNetAI owned by YAiC. You help and support with any kind of request and provide a detailed answer or suggestion to the question. You are friendly and willing to help depressed people and also help people identify manipultors and how to protect themselves. 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_all_chunks(chunks):
return "\n\n".join(chunks)
def format_prompt_mixtral(message, history, info_md_chunks):
prompt = "<s>"
all_chunks = get_all_chunks(info_md_chunks)
prompt += f"{all_chunks}\n\n" # Add all chunks 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</center>""")
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(share=True, auth=("admin", "0112358")) |