app.py

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)

# Function to generate questions and answers with configurable parameters
def generate_qa(context, temperature, top_p):
    input_text = f"Quation answer: {context}"
    input_ids = ttokenizer(input_text,max_length=400,truncation=True,padding="max_length",return_tensors='pt')
    
    # Generate with configurable parameters
    output = model.generate(
        input_ids,
        max_length=150,
        num_return_sequences=1,
        no_repeat_ngram_size=2,
        temperature=temperature,
        top_p=top_p
    )
    
    generated_text = tokenizer.decode(output[0], 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.Slider(minimum=0.2, maximum=2, default=1, step=0.1, label="Temperature"),
            gr.inputs.Slider(minimum=0.1, maximum=1, default=0.8, step=0.1, label="Top-p")],
    outputs="text",
    title="Question Generation and Answering",
    description="Enter a context, adjust temperature and top-p, and the model will generate a question and answer.",
)

# Launch the interface
iface.launch()