app.py

import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the model and tokenizer
model_name = "google/flan-t5-large"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def concatenate_and_generate(text1, text2, temperature, top_p):
    concatenated_text = text1 + " " + text2
    inputs = tokenizer(concatenated_text, return_tensors="pt")
    
    # Generate the output with specified temperature and top_p
    output = model.generate(
        inputs["input_ids"], 
        do_sample=True, 
        temperature=temperature, 
        top_p=top_p,
        max_length=100
    )
    
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    return generated_text

# Define Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Text Concatenation and Generation with FLAN-T5")
    gr.Markdown("Concatenate two input texts and generate an output using google/flan-t5-large. Adjust the temperature and top_p parameters for different generation behaviors.")

    text1 = gr.Textbox(lines=2, placeholder="Enter first text here...")
    text2 = gr.Textbox(lines=2, placeholder="Enter second text here...")
    temperature = gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature")
    top_p = gr.Slider(0.1, 1.0, value=0.9, step=0.1, label="Top-p")
    
    output = gr.Textbox()
    
    btn = gr.Button("Generate")
    btn.click(concatenate_and_generate, [text1, text2, temperature, top_p], output)

demo.launch()