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import gradio as gr
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel, TextDataset, DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
__checkpoint = "gpt2"
__tokenizer = GPT2Tokenizer.from_pretrained(__checkpoint)
__model = GPT2LMHeadModel.from_pretrained(__checkpoint)
__model_output_path = "gpt_model"
# Create a Data collator object
__data_collator = DataCollatorForLanguageModeling(tokenizer=__tokenizer, mlm=False, return_tensors="pt")
#prepare data
def prepareData():
df=pd.read_csv("MedQuAD.csv")
df['Question']=df['Question'].replace(r'^\s*$', np.nan, regex=True)
df['Answer']=df['Answer'].replace(r'^\s*$', np.nan, regex=True)
df = df.drop_duplicates(subset=['Question', 'Answer'])
df=df.dropna()
train_ds=df.groupby('Focus').head(100)
train_ds=train_ds.groupby('Focus').head(4).reset_index(drop=True)
test_ds=train_ds.groupby('Focus').head(1).reset_index(drop=True)
train_seq=list()
for i in range(len(train_ds)):
s='<question>'+train_ds.loc[i,'Question']+'<answer>'+train_ds.loc[i,'Answer']
train_seq.append(s)
val_seq=list()
for i in range(len(test_ds)):
s='<question>'+test_ds.loc[i,'Question']+'<answer>'+test_ds.loc[i,'Answer']
val_seq.append(s)
with open("train.txt", "w") as f:
f.writelines(line+'\n' for line in train_seq)
with open("val.txt", "w") as f:
f.writelines(line+'\n' for line in val_seq)
def fine_tune_gpt():
train_dataset = TextDataset(tokenizer=__tokenizer, file_path="train.txt", block_size=128)
val_dataset = TextDataset(tokenizer=__tokenizer, file_path="val.txt", block_size=128)
training_args = TrainingArguments(
output_dir = __model_output_path,
overwrite_output_dir = True,
per_device_train_batch_size = 2, # try with 2
per_device_eval_batch_size = 2, # try with 2
num_train_epochs = 0.01,
save_steps = 1_000,
save_total_limit = 2,
logging_dir = './logs',
)
# Train the model
trainer = Trainer(
model = __model,
args = training_args,
data_collator = __data_collator,
train_dataset = train_dataset,
eval_dataset = val_dataset,
)
trainer.train()
# Save the model
trainer.save_model(model_output_path)
# Save the tokenizer
__tokenizer.save_pretrained(model_output_path)
def queryGPT(question):
return generate_response(__model, __tokenizer, question)
def generate_response(model,tokenizer, prompt, max_length=200):
input_ids = tokenizer.encode(prompt, return_tensors="pt") # 'pt' for returning pytorch tensor
#my_model = GPT2LMHeadModel.from_pretrained(model_output_path)
#my_tokenizer = GPT2Tokenizer.from_pretrained(model_output_path)
# 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)
with gr.Blocks() as demo:
txt_input = gr.Textbox(label="Input Question", lines=2)
txt_output = gr.Textbox(value="", label="Answer")
btn = gr.Button(value="Submit")
btn.click(queryGPT, inputs=[txt_input], outputs=[txt_output])
if __name__ == "__main__":
#prepareData()
#fine_tune_gpt()
demo.launch() |