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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the tokenizer and model
model_name = "mohamedemam/QA_GeneraTor"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Recommended words for users to choose from
recommended_words = [
"which", "how", "when", "where", "who", "whom", "whose", "why",
"which", "who", "whom", "whose", "whereas",
"can", "could", "may", "might", "will", "would", "shall", "should",
"do", "does", "did", "is", "are", "am", "was", "were", "be", "being", "been",
"have", "has", "had", "if", "is", "are", "am", "was", "were", "do", "does", "did", "can", "could",
"will", "would", "shall", "should", "might", "may", "must",
"may", "might", "must",
]
# Function to generate questions and answers with configurable parameters
def generate_qa(context, recommended_word, temperature, top_p,numofseq):
input_text = f"{recommended_word}: {context.lower()}"
input_ids = tokenizer(input_text, max_length=400, truncation=True, padding="max_length", return_tensors='pt')
# Generate with configurable parameters
output = model.generate(
**input_ids,
temperature=temperature,
top_p=top_p,num_return_sequences=numofseq
)
generated_text = tokenizer.batch_decode(output, skip_special_tokens=True)
return generated_text
# Create the Gradio interface with sliders for temperature and top-p
iface = gr.Interface(
fn=generate_qa,
inputs=[
"text",
gr.inputs.Radio(recommended_words, label="Choose a Recommended Word"),
gr.inputs.Slider(minimum=0, maximum=4, default=1, step=0.05, label="Temperature"),
gr.inputs.Slider(minimum=0.1, maximum=1, default=0.2, step=0.05, label="Top-p"),
gr.inputs.Slider(minimum=1, maximum=50, default=1, step=1, label="num_return_sequences")
],
outputs="text",
title="Question Generation and Answering",
description="Enter a context, choose a recommended word, and adjust temperature and top-p. The model will generate a question and answer.",
)
# Launch the interface
iface.launch() |