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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" | |
# Load model directly | |
#model = "GRMenon/mental-health-mistral-7b-instructv0.2-finetuned-V2" | |
client = InferenceClient(model) | |
# Embedded system prompt | |
system_prompt_text ="You are Phoenix AI Healthcare. You are professional, you are polite, give only truthful information and are based on the Mistral-7B model from Mistral AI about Healtcare and Wellness. You can communicate in different languages equally well." | |
# 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;'>PhoenixAI</h1><br><h3> made with love by Omdena </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) |