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import gradio as gr | |
from gradio.components import Textbox, Checkbox | |
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, T5ForConditionalGeneration | |
from peft import PeftModel, PeftConfig | |
import torch | |
import datasets | |
# Load your fine-tuned model and tokenizer | |
model_name = "google/flan-t5-large" | |
peft_name = "legacy107/flan-t5-large-ia3-cpgQA" | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
pretrained_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large") | |
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large") | |
model = PeftModel.from_pretrained(model, peft_name) | |
peft_name = "legacy107/flan-t5-large-ia3-bioasq-paraphrase" | |
peft_config = PeftConfig.from_pretrained(peft_name) | |
paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
paraphrase_model = PeftModel.from_pretrained(paraphrase_model, peft_name) | |
max_length = 512 | |
max_target_length = 200 | |
# Load your dataset | |
dataset = datasets.load_dataset("minh21/cpgQA-v1.0-unique-context-test-10-percent-validation-10-percent", split="test") | |
dataset = dataset.shuffle() | |
dataset = dataset.select(range(10)) | |
def paraphrase_answer(question, answer, use_pretrained=False): | |
# Combine question and context | |
input_text = f"question: {question}. Paraphrase the answer to make it more natural answer: {answer}" | |
# Tokenize the input text | |
input_ids = tokenizer( | |
input_text, | |
return_tensors="pt", | |
padding="max_length", | |
truncation=True, | |
max_length=max_length, | |
).input_ids | |
# Generate the answer | |
with torch.no_grad(): | |
if use_pretrained: | |
generated_ids = pretrained_model.generate(input_ids=input_ids, max_new_tokens=max_target_length) | |
else: | |
generated_ids = paraphrase_model.generate(input_ids=input_ids, max_new_tokens=max_target_length) | |
# Decode and return the generated answer | |
paraphrased_answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True) | |
return paraphrased_answer | |
# Define your function to generate answers | |
def generate_answer(question, context, ground_truth, do_pretrained, do_natural, do_pretrained_natural): | |
# Combine question and context | |
input_text = f"question: {question} context: {context}" | |
# Tokenize the input text | |
input_ids = tokenizer( | |
input_text, | |
return_tensors="pt", | |
padding="max_length", | |
truncation=True, | |
max_length=max_length, | |
).input_ids | |
# Generate the answer | |
with torch.no_grad(): | |
generated_ids = model.generate(input_ids=input_ids, max_new_tokens=max_target_length) | |
# Decode and return the generated answer | |
generated_answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True) | |
# Paraphrase answer | |
paraphrased_answer = "" | |
if do_natural: | |
paraphrased_answer = paraphrase_answer(question, generated_answer) | |
# Get pretrained model's answer | |
pretrained_answer = "" | |
if do_pretrained: | |
with torch.no_grad(): | |
pretrained_generated_ids = pretrained_model.generate(input_ids=input_ids, max_new_tokens=max_target_length) | |
pretrained_answer = tokenizer.decode(pretrained_generated_ids[0], skip_special_tokens=True) | |
# Get pretrained model's natural answer | |
pretrained_paraphrased_answer = "" | |
if do_pretrained_natural: | |
pretrained_paraphrased_answer = paraphrase_answer(question, generated_answer, True) | |
return generated_answer, paraphrased_answer, pretrained_answer, pretrained_paraphrased_answer | |
# Define a function to list examples from the dataset | |
def list_examples(): | |
examples = [] | |
for example in dataset: | |
context = example["context"] | |
question = example["question"] | |
answer = example["answer_text"] | |
examples.append([question, context, answer, True, True, True]) | |
return examples | |
# Create a Gradio interface | |
iface = gr.Interface( | |
fn=generate_answer, | |
inputs=[ | |
Textbox(label="Question"), | |
Textbox(label="Context"), | |
Textbox(label="Ground truth"), | |
Checkbox(label="Include pretrained model's answer"), | |
Checkbox(label="Include natural answer"), | |
Checkbox(label="Include pretrained model's natural answer") | |
], | |
outputs=[ | |
Textbox(label="Generated Answer"), | |
Textbox(label="Natural Answer"), | |
Textbox(label="Pretrained Model's Answer"), | |
Textbox(label="Pretrained Model's Natural Answer") | |
], | |
examples=list_examples() | |
) | |
# Launch the Gradio interface | |
iface.launch() |