# Using Public space/
import requests
import gradio as gr

# Language dropdown: Display label → (from_code, to_code)
LANGUAGES = {
    "English → Afrikaans": ("en", "af"),
    "English → Xhosa": ("en", "xh"),
    "English → Zulu": ("en", "zu"),
    "English → Sesotho": ("en", "st"),
    "English → Northern Sotho (Sepedi)": ("en", "nso"),
    "English → Tsonga": ("en", "ts"),
    "English → Shona": ("en", "sn"),
    "English → Yoruba": ("en", "yo"),
    "English → Swahili": ("en", "sw"),
}

API_URL = "https://sepioo-facebook-translation.hf.space/translate"


def translate(input_text, language_label):
    from_lang, to_lang = LANGUAGES[language_label]

    payload = {
        "from_language": from_lang,
        "to_language": to_lang,
        "input_text": input_text,
    }

    response = requests.post(API_URL, json=payload)

    if response.status_code != 200:
        return f"Error {response.status_code}: {response.text}"

    try:
        data = response.json()
        if "translate" in data:
            return data["translate"]
        elif "detail" in data:
            return f"Validation error: {data['detail']}"
        else:
            return "Unexpected response format."
    except Exception as e:
        return f"Error parsing response: {e}"


# Gradio UI
translator = gr.Interface(
    fn=translate,
    inputs=[
        gr.Textbox(label="Input Text", placeholder="Type English text here..."),
        gr.Dropdown(list(LANGUAGES.keys()), label="Select Language Target"),
    ],
    outputs=gr.Textbox(label="Translation"),
    title="Translademia",
    description="Translate English to South African languages",
)

translator.launch()


# import requests
# import gradio as gr
# from dotenv import load_dotenv
# from transformers import MBart50TokenizerFast
# import os

# # Load environment variables
# load_dotenv()
# HF_TOKEN = os.getenv("HF_TOKEN")
# headers = {"Authorization": f"Bearer {HF_TOKEN}"}

# # Correct mBART-50 language codes
# LANGUAGES = {
#     "English → Afrikaans": "af_ZA",
#     "English → Xhosa": "xh_ZA",
#     "English → Zulu": "zu_ZA",
#     "English → Sesotho": "st_ZA",  # Southern Sotho
#     "English → Tswana": "tn_ZA",
#     # The following are *not officially* supported by mBART-50 and may raise errors
#     # You can remove them if not working
#     # "English → Northern Sotho": "nso_ZA",
#     # "English → Swati": "ss_ZA",
#     # "English → Tsonga": "ts_ZA",
#     # "English → Venda": "ve_ZA",
# }

# MODEL_NAME = "facebook/mbart-large-50-many-to-many-mmt"
# API_URL = f"https://api-inference.huggingface.co/models/{MODEL_NAME}"

# # Load tokenizer to get language token IDs
# tokenizer = MBart50TokenizerFast.from_pretrained(MODEL_NAME)


# def get_token_id(lang_code):
#     """Return the forced_bos_token_id for the target language."""
#     return tokenizer.lang_code_to_id[lang_code]


# def query(payload):
#     """Send the translation request to the Hugging Face API."""
#     response = requests.post(API_URL, headers=headers, json=payload)

#     if response.status_code != 200:
#         print(f"[ERROR] API failed: {response.status_code} - {response.text}")
#         return {"error": f"Request failed with {response.status_code}"}

#     try:
#         return response.json()
#     except requests.exceptions.JSONDecodeError:
#         print(f"[ERROR] Failed to parse JSON: {response.text}")
#         return {"error": "Invalid JSON from API"}


# def translate(input_text, language_label):
#     """Main translation function."""
#     target_lang_code = LANGUAGES[language_label]
#     token_id = get_token_id(target_lang_code)

#     payload = {
#         "inputs": input_text,
#         "parameters": {"forced_bos_token_id": token_id},
#         "options": {"wait_for_model": True},
#     }

#     response = query(payload)

#     if "error" in response:
#         return f"Error: {response['error']}"

#     return response[0]["translation_text"]


# # Gradio UI
# translator = gr.Interface(
#     fn=translate,
#     inputs=[
#         gr.Textbox(label="Input Text", placeholder="Type text here..."),
#         gr.Dropdown(list(LANGUAGES.keys()), label="Select Language Target"),
#     ],
#     outputs=gr.Textbox(label="Translation"),
#     title="Translademia",
#     description="Translate English text to South African languages using Meta's mBART-50 model.",
# )

# translator.launch()