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
inputs = [
    gr.inputs.Textbox(lines=2, placeholder="Enter first text here..."),
    gr.inputs.Textbox(lines=2, placeholder="Enter second text here..."),
    gr.inputs.Slider(0.1, 1.0, 0.7, step=0.1, label="Temperature"),
    gr.inputs.Slider(0.1, 1.0, 0.9, step=0.1, label="Top-p")
]
outputs = gr.outputs.Textbox()

gr.Interface(
    fn=concatenate_and_generate, 
    inputs=inputs, 
    outputs=outputs, 
    title="Text Concatenation and Generation with FLAN-T5",
    description="Concatenate two input texts and generate an output using google/flan-t5-large. Adjust the temperature and top_p parameters for different generation behaviors."
).launch()