import fasttext
from huggingface_hub import hf_hub_download
import matplotlib.pyplot as plt
import seaborn as sns

# Download the model from Hugging Face Hub
# model_path = hf_hub_download(
#     # repo_id="cis-lmu/glotlid", filename="model.bin", cache_dir="./glotlid"
#     repo_id="facebook/fasttext-language-identification",
#     filename="model.bin",
#     cache_dir="fasttext",
# )
model = fasttext.load_model("fasttext/model.bin")


def identify_languages(title):
    predictions = model.predict(title, k=5)  # Get top 5 predictions
    labels = predictions[0]
    confidences = predictions[1]

    results = []
    for label, confidence in zip(labels, confidences):
        language = label.replace("__label__", "")
        results.append((language, confidence))

    return results


def plot_confidences(results):
    languages = [result[0] for result in results]
    confidences = [result[1] for result in results]

    plt.figure(figsize=(10, 6))
    sns.barplot(x=confidences, y=languages)
    plt.xlabel("Confidence")
    plt.ylabel("Language")
    plt.title("Language Identification Confidence")

    return plt


def identify_and_plot(title):
    results = identify_languages(title)
    plot = plot_confidences(results)
    return results, plot


LID_EXAMPLES = [
    "ပိုၼ်းၵႅပ်ႈလိၵ်ႈလၢႆးတႆး ဢၼ်ၶူးပွင်သွၼ်လူင်လိၵ်ႈလၢႆးတႆး",
    "ယု၀တီဂျင်းဖောမယ်၊ ရှမ်းစာပေသမိုင်းနှင့်",
    "Hello World, မႂ်ႇသုင်ၶႃႈ",
]