import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
import csv

MODEL_URL = "https://huggingface.co/dsfsi/PuoBERTa-News"
WEBSITE_URL = "https://www.kodiks.com/ai_solutions.html"

tokenizer = AutoTokenizer.from_pretrained("dsfsi/PuoBERTa-News")
model = AutoModelForSequenceClassification.from_pretrained("dsfsi/PuoBERTa-News")

categories = {
    "arts_culture_entertainment_and_media": "Botsweretshi, setso, boitapoloso le bobegakgang",
    "crime_law_and_justice": "Bosenyi, molao le bosiamisi",
    "disaster_accident_and_emergency_incident": "Masetlapelo, kotsi le tiragalo ya maemo a tshoganyetso",
    "economy_business_and_finance": "Ikonomi, tsa kgwebo le tsa ditšhelete",
    "education": "Thuto",
    "environment": "Tikologo",
    "health": "Boitekanelo",
    "politics": "Dipolotiki",
    "religion_and_belief": "Bodumedi le tumelo",
    "society": "Setšhaba"
}

# Create a row layout with a centered column for the logo
with gr.Row():
    gr.Column()
    gr.Column(gr.Image(value="logo_transparent_small.png", elem_id="logo", label=None))  # Removed alt parameter
    gr.Column()

description = """
<p style='text-align: center;'>
    Setswana News Classification
</p>
<p>
    This space provides a classification service for news in Setswana.
</p>
"""

article = """
<div style='text-align: center;'>
    <a href='https://github.com/dsfsi/PuoBERTa-News' target='_blank'>GitHub</a> |
    <a href='https://docs.google.com/forms/d/e/1FAIpQLSf7S36dyAUPx2egmXbFpnTBuzoRulhL5Elu-N1eoMhaO7v10w/viewform' target='_blank'>Feedback Form</a> 
</div>
"""

def prediction(news):
    classifier = pipeline("text-classification", tokenizer=tokenizer, model=model, return_all_scores=True)
    preds = classifier(news)
    preds_dict = {categories.get(pred['label'], pred['label']): round(pred['score'], 4) for pred in preds[0]}
    return preds_dict

def file_prediction(file):
    news_list = []

    if file.name.endswith('.csv'):
        file.seek(0)
        reader = csv.reader(file.read().decode('utf-8').splitlines())
        news_list = [row[0] for row in reader if row]
    else:
        file.seek(0)
        file_content = file.read().decode('utf-8')
        news_list = file_content.splitlines()

    results = []
    for news in news_list:
        if news.strip(): 
            pred = prediction(news)
            results.append([news, pred])

    return results

gradio_ui = gr.Interface(
    fn=prediction,
    title="Setswana News Classification",
    description=f"Enter Setswana news article to see the category of the news.\n For this classification, the {MODEL_URL} model was used.",
    inputs=gr.Textbox(lines=10, label="Paste some Setswana news here"),
    outputs=gr.Label(num_top_classes=5, label="News categories probabilities"),
    theme="default",
    article="<p style='text-align: center'>For our other AI works: <a href='https://www.kodiks.com/ai_solutions.html' target='_blank'>https://www.kodiks.com/ai_solutions.html</a> | <a href='https://twitter.com/KodiksBilisim' target='_blank'>Contact us</a></p>",
)

gradio_file_ui = gr.Interface(
    fn=file_prediction,
    title="Upload File for Setswana News Classification",
    description=f"Upload a text or CSV file with Setswana news articles. The first column in the CSV should contain the news text.",
    inputs=gr.File(label="Upload text or CSV file"),
    outputs=gr.Dataframe(headers=["News Text", "Category Predictions"], label="Predictions from file"),
    theme="default" 
)

authors = """
<div style='text-align: center;'>
    Authors: Vukosi Marivate and Moseli Mots'Oehli and Valencia Wagner and Richard Lastrucci and Isheanesu Dzingirai
</div>
"""

citation = """
@inproceedings{marivate2023puoberta,
  title   = {PuoBERTa: Training and evaluation of a curated language model for Setswana},
  author  = {Vukosi Marivate and Moseli Mots'Oehli and Valencia Wagner and Richard Lastrucci and Isheanesu Dzingirai},
  year    = {2023},
  booktitle= {Artificial Intelligence Research. SACAIR 2023. Communications in Computer and Information Science},
  url= {https://link.springer.com/chapter/10.1007/978-3-031-49002-6_17},
  keywords = {NLP},
  preprint_url = {https://arxiv.org/abs/2310.09141},
  dataset_url = {https://github.com/dsfsi/PuoBERTa},
  software_url = {https://huggingface.co/dsfsi/PuoBERTa}
}
"""

doi = """
<div style='text-align: center;'>
    DOI: <a href="https://doi.org/10.1007/978-3-031-49002-6_17" target="_blank">10.1007/978-3-031-49002-6_17</a>
</div>
"""

gradio_combined_ui = gr.TabbedInterface([gradio_ui, gradio_file_ui], ["Text Input", "File Upload"])

gradio_combined_ui.launch()