import gradio as gr
import re
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from nltk.corpus import stopwords
from spaces import GPU
import nltk

# Download stopwords if not already
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))

# Model choices
model_choices = {
    "Pegasus (google/pegasus-xsum)": "google/pegasus-xsum",
    "BigBird-Pegasus (google/bigbird-pegasus-large-arxiv)": "google/bigbird-pegasus-large-arxiv",
    "LongT5 Large (google/long-t5-tglobal-large)": "google/long-t5-tglobal-large",
    "BART Large CNN (facebook/bart-large-cnn)": "facebook/bart-large-cnn",
    "ProphetNet (microsoft/prophetnet-large-uncased-cnndm)": "microsoft/prophetnet-large-uncased-cnndm",
    "LED (allenai/led-base-16384)": "allenai/led-base-16384",
    "T5 Large (t5-large)": "t5-large",
    "Flan-T5 Large (google/flan-t5-large)": "google/flan-t5-large",
    "DistilBART CNN (sshleifer/distilbart-cnn-12-6)": "sshleifer/distilbart-cnn-12-6",
    "DistilBART XSum (mrm8488/distilbart-xsum-12-6)": "mrm8488/distilbart-xsum-12-6",
    "T5 Base (t5-base)": "t5-base",
    "Flan-T5 Base (google/flan-t5-base)": "google/flan-t5-base",
    "BART CNN SamSum (philschmid/bart-large-cnn-samsum)": "philschmid/bart-large-cnn-samsum",
    "T5 SamSum (knkarthick/pegasus-samsum)": "knkarthick/pegasus-samsum",
    "LongT5 Base (google/long-t5-tglobal-base)": "google/long-t5-tglobal-base",
    "T5 Small (t5-small)": "t5-small",
    "MBART (facebook/mbart-large-cc25)": "facebook/mbart-large-cc25",
    "MarianMT (Helsinki-NLP/opus-mt-en-ro)": "Helsinki-NLP/opus-mt-en-ro",
    "Falcon Instruct (tiiuae/falcon-7b-instruct)": "tiiuae/falcon-7b-instruct",
    "BART ELI5 (yjernite/bart_eli5)": "yjernite/bart_eli5"
}

model_cache = {}

def clean_text(input_text):
    cleaned_text = re.sub(r'[^A-Za-z0-9\s]', ' ', input_text)
    words = cleaned_text.split()
    words = [word for word in words if word.lower() not in stop_words]
    return " ".join(words).strip()

def load_model(model_name):
    if model_name not in model_cache:
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForSeq2SeqLM.from_pretrained(
            model_name,
            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
        )
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        model = model.to(device)
        model_cache[model_name] = (tokenizer, model)

        # Warm up with dummy input
        dummy_input = tokenizer("summarize: hello world", return_tensors="pt").input_ids.to(device)
        model.generate(dummy_input, max_length=10)

    return model_cache[model_name]

@GPU  # 👈 Required for ZeroGPU to allocate GPU when this is called
def summarize_text(input_text, model_label, char_limit):
    if not input_text.strip():
        return "Please enter some text."

    input_text = clean_text(input_text)
    model_name = model_choices[model_label]
    tokenizer, model = load_model(model_name)

    if "t5" in model_name.lower() or "flan" in model_name.lower():
        input_text = "summarize: " + input_text

    device = model.device
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
    input_ids = inputs["input_ids"].to(device)

    summary_ids = model.generate(
        input_ids,
        max_length=30,
        min_length=5,
        do_sample=False
    )

    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary[:char_limit].strip()

# Gradio UI
iface = gr.Interface(
    fn=summarize_text,
    inputs=[
        gr.Textbox(lines=6, label="Enter text to summarize"),
        gr.Dropdown(choices=list(model_choices.keys()), label="Choose summarization model", value="Pegasus (google/pegasus-xsum)"),
        gr.Slider(minimum=30, maximum=200, value=80, step=1, label="Max Character Limit")
    ],
    outputs=gr.Textbox(lines=3, label="Summary (truncated to character limit)"),
    title="Multi-Model Text Summarizer (GPU Ready)",
    description="Summarize long or short texts using state-of-the-art Hugging Face models with GPU acceleration (ZeroGPU-compatible)."
)

iface.launch()