import gradio as gr
from transformers import pipeline
from langdetect import detect_langs
import yake

# --- Lazy global pipelines to avoid reloading ---
_pipes = {}

def get_pipe(task, model=None):
    key = (task, model or "")
    if key not in _pipes:
        if model is None:
            _pipes[key] = pipeline(task)
        else:
            _pipes[key] = pipeline(task, model=model)
    return _pipes[key]

# --- Utilities ---
def safe_text(txt: str) -> str:
    return (txt or "").strip()

def detect_language(text: str):
    text = safe_text(text)
    if not text or len(text.split()) < 3:
        return "❌ Please provide a longer text (at least 3 words)."
    try:
        langs = detect_langs(text)
        results = [f"{str(l.lang).upper()} — {l.prob:.2f}" for l in langs[:3]]
        return " / ".join(results)
    except Exception as e:
        return f"⚠️ Could not detect language: {e}"

def summarize_text(text: str, target_ratio: float = 0.25, min_words: int = 30, max_words: int = 160):
    text = safe_text(text)
    if not text or len(text.split()) < 50:
        return "❌ Please paste a longer text (50+ words) to summarize."
    # Heuristic: map words to token-ish lengths
    n_words = len(text.split())
    approx_tokens = int(n_words * 1.3)
    max_new_tokens = max(int(approx_tokens * target_ratio), 64)
    max_new_tokens = min(max_new_tokens, int(max_words * 1.3))
    min_length = int(max_new_tokens * 0.5)

    summarizer = get_pipe("summarization", model="sshleifer/distilbart-cnn-12-6")
    try:
        out = summarizer(
            text,
            max_length=max_new_tokens,
            min_length=min_length,
            do_sample=False,
            truncation=True,
        )[0]["summary_text"]
        return out
    except Exception as e:
        return f"⚠️ Summarization error: {e}"

def extract_keywords(text: str, top_k: int = 10, lang_hint: str = "auto"):
    text = safe_text(text)
    if not text or len(text.split()) < 20:
        return "❌ Please provide at least 20 words for keyword extraction."
    language = None if lang_hint == "auto" else lang_hint
    try:
        kw_extractor = yake.KeywordExtractor(lan=language or "en", n=1, top=top_k)
        keywords = kw_extractor.extract_keywords(text)
        keywords_sorted = sorted(keywords, key=lambda x: x[1])
        lines = [f"{term}  —  score: {score:.4f}" for term, score in keywords_sorted]
        return "\n".join(lines)
    except Exception as e:
        return f"⚠️ Keyword extraction error: {e}"

def analyze_sentiment(text: str):
    text = safe_text(text)
    if not text:
        return "❌ Please enter some text."
    clf = get_pipe("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
    try:
        res = clf(text)[0]
        label = res["label"].upper()
        score = float(res["score"])
        emoji_map = {
            "POSITIVE": "😊🌟🎉",
            "NEGATIVE": "😞💔👎",
            "NEUTRAL":  "😐🤔",
        }
        if score < 0.60:
            label = "NEUTRAL"
        return f"{emoji_map.get(label, '🤷‍♂️')}  ({label}, confidence: {score:.2f})"
    except Exception as e:
        return f"⚠️ Sentiment error: {e}"

with gr.Blocks(title="Smart Text Toolbox") as demo:
    gr.Markdown(
        """
        # Smart Text Toolbox
        A multi-tool NLP demo for education and research. Runs on CPU.
        """
    )

    with gr.Tab("Language Detection"):
        ld_in = gr.Textbox(label="Input text", lines=6, placeholder="Paste a paragraph in any language...")
        ld_btn = gr.Button("Detect Language")
        ld_out = gr.Textbox(label="Detected languages (top-3)", lines=2)
        ld_btn.click(detect_language, inputs=ld_in, outputs=ld_out)

    with gr.Tab("Summarization"):
        sm_in = gr.Textbox(label="Input text (50+ words)", lines=10, placeholder="Paste a long article or paragraph...")
        with gr.Row():
            sm_ratio = gr.Slider(0.1, 0.6, value=0.25, step=0.05, label="Compression ratio target")
        sm_btn = gr.Button("Summarize")
        sm_out = gr.Textbox(label="Summary", lines=10)
        sm_btn.click(summarize_text, inputs=[sm_in, sm_ratio], outputs=sm_out)

    with gr.Tab("Keyword Extraction"):
        kw_in = gr.Textbox(label="Input text (20+ words)", lines=8, placeholder="Paste a paragraph...")
        with gr.Row():
            kw_topk = gr.Slider(5, 20, value=10, step=1, label="Top-K keywords")
            kw_lang = gr.Dropdown(
                label="Language (hint)",
                choices=["auto","en","it","es","fr","de","pt","nl","sv","no","da","fi","pl","cs","sk","sl","hr","ro","hu","tr"],
                value="auto"
            )
        kw_btn = gr.Button("Extract Keywords")
        kw_out = gr.Textbox(label="Keywords", lines=10)
        kw_btn.click(extract_keywords, inputs=[kw_in, kw_topk, kw_lang], outputs=kw_out)

    with gr.Tab("Sentiment Analysis"):
        st_in = gr.Textbox(label="Input text", lines=4, placeholder="Type a sentence...")
        st_btn = gr.Button("Analyze Sentiment")
        st_out = gr.Textbox(label="Sentiment", lines=2)
        st_btn.click(analyze_sentiment, inputs=st_in, outputs=st_out)

if __name__ == "__main__":
    demo.launch()