import gradio as gr
import matplotlib.pyplot as plt
from transformers import pipeline
import langdetect

# Laad de modellen
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en")
classifier = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")

# Functie om meerdere zinnen te vertalen en analyseren
def analyze_multilingual_sentences(text):
    if not text.strip():
        return "<p style='color:red;'><b>⚠️ Enter some text to analyze.</b></p>", None

    # Splits de input op nieuwe regels (elke regel is een aparte zin)
    sentences = [s.strip() for s in text.split("\n") if s.strip()]
    
    # Detecteer de taal van de eerste zin als referentie
    detected_lang = langdetect.detect(sentences[0]) if sentences else "en"

    # Vertaal alleen als de tekst NIET in het Engels is
    if detected_lang != "en":
        translated_sentences = [translator(sentence)[0]['translation_text'] for sentence in sentences]
    else:
        translated_sentences = sentences

    # Voer sentimentanalyse uit op de (vertaalde) tekst
    results = classifier(translated_sentences)

    # Tel het aantal positieve, negatieve en neutrale resultaten
    positive_count = sum(1 for r in results if r['label'] == "LABEL_2")  # POSITIVE
    negative_count = sum(1 for r in results if r['label'] == "LABEL_0")  # NEGATIVE
    neutral_count = len(results) - (positive_count + negative_count)  # NEUTRAL

    # Maak een overzichtelijke output met aangepaste teksten
    output = "<h3>🌍 Sentiment Analysis Results:</h3><br>"
    for original, translated, result in zip(sentences, translated_sentences, results):
        sentiment_label = result['label']
        score = result['score']

        # Aangepaste tekst per sentiment
        if sentiment_label == "LABEL_2":
            sentiment = "WOW! Couldn't feel better."
            color = "green"
        elif sentiment_label == "LABEL_0":
            sentiment = "So sorry ... What could make you feel better?"
            color = "red"
        else:
            sentiment = "Just neutral today."
            color = "blue"

        output += f"<p><b>📌 '{original}'</b></p>"
        if detected_lang != "en":  # Alleen vertaling tonen als invoer niet in het Engels is
            output += f"<p>🔄 <i>Translation:</i> {translated}</p>"
        output += f"<p style='color: {color}; font-weight: bold;'>📊 {sentiment} ({score:.2f})</p><hr>"

    # Maak een grafiek met de sentimentverdeling
    labels = ["Positive", "Neutral", "Negative"]
    values = [positive_count, neutral_count, negative_count]
    colors = ["#FFA500", "#2196F3", "#F44336"]  # Oranje, Blauw, Rood

    fig, ax = plt.subplots()
    ax.pie(values, labels=labels, autopct='%1.1f%%', startangle=90, colors=colors)
    ax.axis("equal")  # Gelijke assen voor een ronde taartdiagram

    return output, fig

# Voorbeeldzinnen
example_sentences_top = [
    "I just won the lottery!",
    "My phone battery died in the middle of an important call.",
    "This weather is so boring."
]

example_sentences_bottom = [
    "I woke up at 5 AM and went for a run.",
    "This is the worst movie I have ever seen.",
    "Just got a puppy, and I'm in love!",
    "The internet is so slow today.",
    "I spilled coffee on my laptop, disaster!",
    "I finally finished my project, time to celebrate!"
]

# Functie om een voorbeeldzin toe te voegen aan het invoerveld zonder te overschrijven
def add_example_text(current_text, example):
    if current_text.strip():
        return f"{current_text}\n{example}"
    return example

# Gradio-interface met duidelijke instructies en styling
with gr.Blocks(css=".orange-btn {background-color: #FFA500 !important; color: black !important; font-weight: bold; font-size: 16px; padding: 10px 20px; border-radius: 8px; border: none; cursor: pointer;} .orange-btn:hover {background-color: #FF8C00 !important;}") as demo:
    gr.Markdown(
        """
        <div style='font-size: 18px; font-weight: bold; text-align: center; color: #333;'>
            Enter sentences in any language, <b>one per line</b>. 
            This AI-powered app translates, analyzes the vibe, and shows the results in a cool summary & chart.
        </div>
        """
    )

    input_box = gr.Textbox(
        lines=5,
        placeholder="Hey there! Drop some sentences (one per line) and get instant sentiment vibes—positive, neutral, or negative...",
        label="Enter your own sentences"
    )

    # Voorbeeldzinnen bovenaan (toevoegen aan invoerveld)
    gr.Markdown("<h3>💡 Or try these sentences:</h3>")
    with gr.Row():
        for example in example_sentences_top:
            gr.Button(example).click(add_example_text, inputs=[input_box, gr.Textbox(value=example, visible=False)], outputs=input_box, queue=False)

    # Oranje knop voor sentimentanalyse (correct toegepast)
    analyze_button = gr.Button("Tell me how I feel", elem_classes="orange-btn")
    output_box = gr.HTML(label="Results")
    plot_box = gr.Plot(label="Sentiment Distribution")

    analyze_button.click(analyze_multilingual_sentences, inputs=input_box, outputs=[output_box, plot_box])
    
    # Voorbeeldzinnen onderaan (toevoegen aan invoerveld)
    gr.Markdown("<h3>💡 Or try more sentences:</h3>")
    with gr.Row():
        for example in example_sentences_bottom:
            gr.Button(example).click(add_example_text, inputs=[input_box, gr.Textbox(value=example, visible=False)], outputs=input_box, queue=False)

# Start de app
demo.launch(share=True)