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import gradio as gr
import time
from transformers import AutoTokenizer
import transformers
import torch
global tokenizer, pipeline
model = "shaneperry0101/Health-Llama-3.2-1B"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
def chat_response(message):
system_message="Welcome! You're now communicating with an AI model trained to assist with information about general health disease. Feel free to ask about causes, symptoms, medications, and treatment options!"
prompt = f"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n{message}[/INST]"
sequences = pipeline(
f'[INST] {prompt} [/INST]',
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=500,
)
for seq in sequences:
bot_message = seq['generated_text'].replace(prompt, '').split('[/INST]')[-1]
return bot_message
with gr.Blocks() as demo:
chatbot = gr.Chatbot(label="Personal Health Assistant")
msg = gr.Textbox()
clear = gr.ClearButton([msg, chatbot])
def respond(message, chat_history):
bot_message = chat_response(message)
chat_history.append((message, bot_message))
time.sleep(2)
return "", chat_history
msg.submit(respond, [msg, chatbot], [msg, chatbot])
if __name__ == "__main__":
demo.launch()
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