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import re
import torch
import gradio as gr
from transformers import GPT2LMHeadModel, GPT2Tokenizer
# Load the model and tokenizer from Hugging Face repository
model_repo_id = "Ajay12345678980/QA_bot" # Replace with your model repository ID
# Initialize the model and tokenizer
model = GPT2LMHeadModel.from_pretrained(model_repo_id)
tokenizer = GPT2Tokenizer.from_pretrained(model_repo_id)
# Define the prediction function
def generate_answer(question):
input_ids = tokenizer.encode(question, return_tensors="pt").to("cuda")
# Create the attention mask and pad token id
attention_mask = torch.ones_like(input_ids).to("cuda")
pad_token_id = tokenizer.eos_token_id
#output = model[0].generate(
output = model.generate(
input_ids,
max_new_tokens=100,
num_return_sequences=1,
attention_mask=attention_mask,
pad_token_id=pad_token_id
)
decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
start_index = decoded_output.find("Answer")
end_index = decoded_output.find("<ANSWER_ENDED>")
if end_index != -1:
# Extract the text between "Answer" and "<ANSWER_ENDED>"
answer_text = decoded_output[start_index + len("Answer"):end_index].strip()
return answer_text
else:
# If "<ANSWER_ENDED>" is not found, return the text following "Answer"
answer_text = decoded_output[start_index + len("Answer"):].strip()
return answer_text
#return tokenizer.decode(output[0], skip_special_tokens=True)
#return tokenizer.decode(output, skip_special_tokens=True)
# Gradio interface setup
interface = gr.Interface(
fn=predict,
inputs="text",
outputs="text",
title="GPT-2 Text Generation",
description="Enter some text and see what the model generates!"
)
# Launch the Gradio app
if __name__ == "__main__":
interface.launch()
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