app.py

"""
M6_NB_MiniProject_1_PartB_Deploy_Medical_Q&A_GPT2.ipynb
Original file in google drive
"""
import os
import gradio as gr
import torch
import transformers
from transformers import AutoModelWithLMHead, AutoTokenizer

def generate_response(model, tokenizer, prompt, max_length=512):
    # YOUR CODE HERE ...
    input_ids = tokenizer.encode(prompt, return_tensors="pt")      # 'pt' for returning pytorch tensor

    # Create the attention mask and pad token id
    attention_mask = torch.ones_like(input_ids)
    pad_token_id = tokenizer.eos_token_id

    output = model.generate(
        input_ids,
        max_length=max_length,
        num_return_sequences=1,
        attention_mask=attention_mask,
        pad_token_id=pad_token_id
    )

    return tokenizer.decode(output[0], skip_special_tokens=True)

def generate_query_response(prompt, max_length=200):
    # Load your model from hub
    model = AutoModelWithLMHead.from_pretrained("vanim/chatgpt2-medical-QnA")
    # Load your tokenizer from hub
    tokenizer = AutoTokenizer.from_pretrained("vanim/chatgpt2-medical-QnA")

    return generate_response(model, tokenizer, prompt, max_length)


# Create title, description and article strings
title = "Medical QnA chat bot"
description = "ChatGPT2 based Medical Q and A demo"

demo = gr.Interface(fn=generate_query_response, 
                    inputs = "text",
                    outputs = "text",
                    title=title,
                    description=description,)

#Launch the demo
demo.launch()