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# -*- coding: utf-8 -*-
"""gradio_deploy.ipynb
Automatically generated by Colaboratory.
"""
import os
import gradio
import torch
from transformers import AutoModelWithLMHead, AutoTokenizer
import numpy as np
loaded_model = AutoModelWithLMHead.from_pretrained("runaksh/medquad-finetuned-gpt2")
loaded_tokenizer = AutoTokenizer.from_pretrained("runaksh/medquad-finetuned-gpt2")
def generate_query_response(prompt, max_length=200):
model = loaded_model
tokenizer = loaded_tokenizer
input_ids = tokenizer.encode(prompt, return_tensors="pt")
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])
title = "Question and Answer"
description = "Medical Question and Answer"
# Gradio elements
# Input from user
in_prompt = gradio.components.Textbox(lines=2, label='Enter the question')
in_max_length = gradio.components.Number(label='Enter the max length')
# Output response
out_response = gradio.components.Textbox(label='Answer')
# Gradio interface to generate UI link
iface = gradio.Interface(fn=generate_query_response,
inputs = [in_prompt,in_max_length],
outputs = out_response,
title=title,
description=description
)
iface.launch(debug = True)
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