import gradio as gr
import torch
import pandas as pd
from transformers import T5ForConditionalGeneration, T5Tokenizer
import io

# Load the pre-trained T5 model for paraphrasing
model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_paraphraser')
tokenizer = T5Tokenizer.from_pretrained('t5-base')

def generate_paraphrases(text, num_return_sequences=5, num_beams=10):
    input_text = "paraphrase: " + text + " </s>"
    encoding = tokenizer.encode_plus(input_text, return_tensors="pt")
    input_ids = encoding["input_ids"]

    outputs = model.generate(
        input_ids,
        max_length=256,
        num_beams=num_beams,
        num_return_sequences=num_return_sequences,
        no_repeat_ngram_size=2,
        early_stopping=True
    )

    paraphrases = [
        tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
        for output in outputs
    ]
    return paraphrases

def generate_and_export(text):
    paraphrases = generate_paraphrases(text)
    df = pd.DataFrame({
        "Original": [text] * len(paraphrases),
        "Perspective": paraphrases
    })
    csv_buffer = io.StringIO()
    df.to_csv(csv_buffer, index=False)
    csv_bytes = csv_buffer.getvalue().encode()
    return paraphrases, csv_bytes

def gradio_generate(text):
    paraphrases, csv_bytes = generate_and_export(text)
    output_text = "\n\n".join(f"Perspective {i+1}: {p}" for i, p in enumerate(paraphrases))
    return output_text, (csv_bytes, "generated_perspectives.csv")

iface = gr.Interface(
    fn=gradio_generate,
    inputs=gr.Textbox(lines=5, placeholder="Enter text here..."),
    outputs=[
        gr.Textbox(label="Generated Perspectives"),
        gr.File(label="Download CSV")
    ],
    title="Paraphrase Perspective Generator",
    description="Enter any text and generate alternative perspectives (paraphrases). Download the results as a CSV file."
)

iface.launch()