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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([32, 7, 92, 8, 108, 51, 64, 84, 93, 94])
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() |