import gradio as gr
from transformers import pipeline
from deep_translator import GoogleTranslator
from langdetect import detect
import os

# =========================================================
# LOAD MODEL ONCE
# =========================================================
summarizer = pipeline(
    "summarization",
    model="karim91/Multilingual_Text_Summarization_System-BART_v1.0.9",
)

# =========================================================
# SUPPORTED LANGUAGES AND LENGTH
# =========================================================
SUPPORTED_LANGUAGES = {
    "Français": "fr",
    "English": "en",
    "Arabic": "ar",
    "Spanish": "es",
    "German": "de",
}

LENGTH_MAP = {
    "Short ": (60, 100),
    "Medium ": (120, 180),
    "Long ": (220, 300),
}

# =========================================================
# CORE FUNCTION
# =========================================================
def summarize_text_multilingual(text, file, output_lang, length_choice):
    # Read text from uploaded file if provided
    if file is not None:
        with open(file.name, "r", encoding="utf-8", errors="ignore") as f:
            text = f.read()

    if text is None or len(text.strip()) < 50:
        return "❌ Please provide at least 50 characters of text."

    min_len, max_len = LENGTH_MAP[length_choice]

    # Detect input language
    source_lang = detect(text)

    # Translate input to English if not English
    text_en = text
    if source_lang != "en":
        try:
            text_en = GoogleTranslator(source=source_lang, target="en").translate(text)
        except Exception as e:
            return f"❌ Error translating input to English: {e}"

    # Summarize in English
    try:
        summary_en = summarizer(
            text_en,
            min_length=min_len,
            max_length=max_len,
            do_sample=False,
        )[0]["summary_text"]
    except Exception as e:
        return f"❌ Error during summarization: {e}"

    # Translate summary to target language if needed
    target_code = SUPPORTED_LANGUAGES.get(output_lang, "en")
    summary = summary_en
    if target_code != "en":
        try:
            summary = GoogleTranslator(source="en", target=target_code).translate(summary_en)
        except Exception as e:
            return f"❌ Error translating summary: {e}"

    return summary

# =========================================================
# GRADIO UI
# =========================================================
with gr.Blocks(
    title="Multilingual Text Summarization",
    theme=gr.themes.Soft(),
) as demo:

    gr.Markdown("""
    # 📝 Multilingual Text Summarization
    ### 🌍 100+ languages | 📊 Demo Hugging Face Spaces
    """)

    with gr.Row():
        with gr.Column(scale=2):
            text_input = gr.Textbox(
                label="📝 Texte à résumer",
                placeholder="Collez votre texte ici (minimum 50 caractères)...",
                lines=10,
            )

            file_input = gr.File(
                label="📁 Ou importez un fichier texte",
                file_types=[".txt", ".md"],
            )

        with gr.Column(scale=1):
            output_lang = gr.Dropdown(
                choices=list(SUPPORTED_LANGUAGES.keys()),
                value="Français",
                label="🌍 Langue de sortie",
            )

            length_choice = gr.Radio(
                choices=list(LENGTH_MAP.keys()),
                value="Moyen (≈150 mots)",
                label="📏 Longueur du résumé",
            )

            submit_btn = gr.Button("🚀 Générer le résumé", variant="primary")

    output = gr.Textbox(
        label="✨ Résumé généré",
        lines=8,
    )

    submit_btn.click(
        fn=summarize_text_multilingual,
        inputs=[text_input, file_input, output_lang, length_choice],
        outputs=output,
    )

# =========================================================
# LAUNCH
# =========================================================
demo.launch()