app.py

from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr
import torch


title = "Ayurveda4U"
description = "LLM-Powered Medical Chatbot that will answer all your health-related queries with the help of Ayurvedic texts ynder the hood!"
examples = [["How can you cure common cold using Ayurveda?"], ["What is the Ayurvedic equivalent of Paracetamol?"]]

model_path = 'tloen/alpaca-lora-7b' #'microsoft/phi-1_5'#'microsoft/DialoGPT-large' #'microsoft/biogpt' #'microsoft/BioGPT-large' #microsoft/DialoGPT-large

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)


def predict(input, history=[]):
    # tokenize the new input sentence
    new_user_input_ids = tokenizer.encode(
        input + tokenizer.eos_token, return_tensors="pt"
    )

    # append the new user input tokens to the chat history
    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)

    # generate a response
    history = model.generate(
        bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id
    ).tolist()

    # convert the tokens to text, and then split the responses into lines
    response = tokenizer.decode(history[0]).split("<|endoftext|>")
    # print('decoded_response-->>'+str(response))
    response = [
        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
    ]  # convert to tuples of list
    # print('response-->>'+str(response))
    return response, history


gr.Interface(
    fn=predict,
    title=title,
    description=description,
    examples=examples,
    inputs=["text", "state"],
    outputs=["chatbot", "state"],
    theme="finlaymacklon/boxy_violet",
).launch()