"""
Gradio Space for Turkish Sentiment Analysis (Fine-tuned Model)
Hugging Face Space için modern Gradio uygulaması
"""
import os
import sys
from pathlib import Path
import gradio as gr
import torch
import pandas as pd

# Add parent directory to path for local imports
sys.path.insert(0, str(Path(__file__).parent))

from app.inference.predictor import SentimentPredictor

# Model name - Fine-tuned model
MODEL_NAME = "codealchemist01/turkish-sentiment-analysis-finetuned"

# Initialize predictor (will be loaded on first use)
predictor = None

def load_predictor():
    """Load predictor (lazy loading)"""
    global predictor
    if predictor is None:
        print(f"[INFO] Loading fine-tuned model from Hugging Face: {MODEL_NAME}")
        predictor = SentimentPredictor(model_name=MODEL_NAME)
        print("[OK] Model loaded successfully!")
    return predictor

def analyze_sentiment(text, show_details=False):
    """
    Analyze sentiment for a single text
    
    Args:
        text: Input text string
        show_details: If True, show detailed probabilities
    
    Returns:
        Formatted result string, confidence dict, and probabilities for chart
    """
    if not text or not text.strip():
        return "⚠️ Lütfen analiz etmek için bir metin girin.", {"Confidence": 0}, pd.DataFrame({"Sentiment": [], "Probability": []})
    
    try:
        pred = load_predictor()
        result = pred.predict(text.strip(), return_probabilities=True)
        
        label = result['label'].upper()
        confidence = result['confidence'] * 100
        probs = result.get('probabilities', {})
        
        # Emoji mapping
        emoji_map = {"POSITIVE": "😊", "NEGATIVE": "😞", "NEUTRAL": "😐"}
        emoji = emoji_map.get(label, "📊")
        
        # Format result
        result_text = f"## {emoji} {label}\n\n"
        result_text += f"**Güven Oranı:** {confidence:.2f}%\n\n"
        
        if show_details:
            result_text += "**Detaylı Olasılıklar:**\n"
            # Sort probabilities
            sorted_probs = sorted(probs.items(), key=lambda x: x[1], reverse=True)
            for prob_label, prob_value in sorted_probs:
                prob_percent = prob_value * 100
                bar_length = int(prob_value * 25)  # 25 chars max
                bar = "█" * bar_length + "░" * (25 - bar_length)
                result_text += f"- **{prob_label.upper()}:** {bar} {prob_percent:.2f}%\n"
        
        # Confidence dict for Label component
        confidence_dict = {label: confidence}
        
        # DataFrame for BarPlot
        prob_df = pd.DataFrame({
            "Sentiment": [k.upper() for k in probs.keys()],
            "Probability": [v * 100 for v in probs.values()]
        })
        
        return result_text, confidence_dict, prob_df
        
    except Exception as e:
        return f"❌ Hata: {str(e)}", {"Error": 0}, pd.DataFrame({"Sentiment": [], "Probability": []})

def analyze_batch(texts):
    """
    Analyze sentiment for multiple texts
    
    Args:
        texts: Newline-separated text strings
    
    Returns:
        Formatted results
    """
    if not texts or not texts.strip():
        return "⚠️ Lütfen analiz etmek için metinler girin."
    
    # Split texts
    text_list = [t.strip() for t in texts.strip().split('\n') if t.strip()]
    
    if not text_list:
        return "⚠️ Lütfen en az bir metin girin."
    
    try:
        pred = load_predictor()
        results = []
        
        correct_count = {"positive": 0, "negative": 0, "neutral": 0}
        total_count = {"positive": 0, "negative": 0, "neutral": 0}
        
        for i, text in enumerate(text_list, 1):
            try:
                result = pred.predict(text, return_probabilities=True)
                label = result['label'].upper()
                confidence = result['confidence'] * 100
                
                emoji_map = {"POSITIVE": "😊", "NEGATIVE": "😞", "NEUTRAL": "😐"}
                emoji = emoji_map.get(label, "📊")
                
                results.append(f"### {i}. {emoji} {label} ({confidence:.2f}%)\n")
                results.append(f"**Metin:** {text}\n\n")
                
            except Exception as e:
                results.append(f"### {i}. ❌ Hata\n")
                results.append(f"**Metin:** {text}\n")
                results.append(f"**Hata:** {str(e)}\n\n")
        
        summary = f"\n\n---\n**📊 Özet:** {len(text_list)} metin analiz edildi."
        return "".join(results) + summary
        
    except Exception as e:
        return f"❌ Hata: {str(e)}"

# Example texts
EXAMPLE_TEXTS = {
    "positive": [
        "Bu ürün gerçekten harika! Çok memnun kaldım.",
        "Mükemmel bir deneyimdi, kesinlikle tekrar kullanacağım!",
        "Hızlı kargo, kaliteli ürün, çok başarılı!",
    ],
    "negative": [
        "Hizmet çok kötüydü, hiç beğenmedim.",
        "Ürün beklentilerimi karşılamadı, hayal kırıklığına uğradım.",
        "Kargo çok geç geldi, paket hasarlıydı.",
    ],
    "neutral": [
        "Ürün normal, beklediğim gibi. Özel bir şey yok.",
        "Kargo zamanında geldi, paket sağlamdı. Standart bir teslimat.",
        "Fiyat uygun, kalite orta seviye. Ne iyi ne kötü.",
    ]
}

# Custom CSS for modern design
css = """
.gradio-container {
    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
}
.main-header {
    text-align: center;
    padding: 30px;
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    color: white;
    border-radius: 15px;
    margin-bottom: 20px;
    box-shadow: 0 4px 6px rgba(0,0,0,0.1);
}
.info-box {
    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
    padding: 20px;
    border-radius: 12px;
    margin: 15px 0;
    border-left: 4px solid #667eea;
}
.metric-box {
    background: white;
    padding: 15px;
    border-radius: 10px;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
    margin: 10px 0;
}
.example-btn {
    margin: 5px;
}
"""

# Create Gradio interface
with gr.Blocks(css=css, theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue")) as demo:
    # Header
    gr.Markdown(
        """
        <div class="main-header">
            <h1>🇹🇷 Turkish Sentiment Analysis</h1>
            <h3>Fine-tuned Model - Geliştirilmiş Neutral Algılama</h3>
            <p>Daha iyi performans için fine-tuned edilmiş model</p>
        </div>
        """,
        elem_classes=["main-header"]
    )
    
    # Model info
    with gr.Row():
        with gr.Column(scale=2):
            gr.Markdown(
                """
                <div class="info-box">
                    <h3>📊 Model Bilgileri</h3>
                    <p><strong>Model:</strong> <a href="https://huggingface.co/codealchemist01/turkish-sentiment-analysis-finetuned" target="_blank">turkish-sentiment-analysis-finetuned</a></p>
                    <p><strong>Base Model:</strong> <a href="https://huggingface.co/codealchemist01/turkish-sentiment-analysis" target="_blank">codealchemist01/turkish-sentiment-analysis</a></p>
                    <p><strong>Fine-tuning:</strong> Balanced dataset (556,888 samples) ile eğitildi</p>
                </div>
                """
            )
        with gr.Column(scale=1):
            gr.Markdown(
                """
                <div class="metric-box">
                    <h4>🎯 Performans</h4>
                    <p><strong>Accuracy:</strong> 91.96%</p>
                    <p><strong>Neutral F1:</strong> 90.57% ⬆️</p>
                    <p><strong>Positive F1:</strong> 94.61%</p>
                    <p><strong>Negative F1:</strong> 88.68%</p>
                </div>
                """
            )
    
    # Main tabs
    with gr.Tabs():
        # Single text analysis tab
        with gr.Tab("📝 Tek Metin Analizi"):
            gr.Markdown("### Tek bir metin için detaylı duygu analizi yapın")
            
            with gr.Row():
                with gr.Column(scale=2):
                    text_input = gr.Textbox(
                        label="Metin",
                        placeholder="Analiz etmek istediğiniz Türkçe metni buraya yazın...",
                        lines=5,
                        info="Türkçe bir metin yazın ve analiz edin."
                    )
                    
                    with gr.Row():
                        analyze_btn = gr.Button("🔍 Analiz Et", variant="primary", size="lg")
                        show_details = gr.Checkbox(label="Detaylı olasılıkları göster", value=True)
                    
                    # Example texts
                    gr.Markdown("### 📌 Örnek Metinler")
                    
                    # Helper function to create example click handlers
                    def create_example_handler(example_text):
                        def handler():
                            return example_text
                        return handler
                    
                    with gr.Row():
                        with gr.Column():
                            gr.Markdown("**Pozitif Örnekler:**")
                            for example in EXAMPLE_TEXTS["positive"]:
                                btn = gr.Button(example[:40] + "...", size="sm", variant="secondary")
                                btn.click(fn=create_example_handler(example), outputs=text_input)
                        
                        with gr.Column():
                            gr.Markdown("**Neutral Örnekler:**")
                            for example in EXAMPLE_TEXTS["neutral"]:
                                btn = gr.Button(example[:40] + "...", size="sm", variant="secondary")
                                btn.click(fn=create_example_handler(example), outputs=text_input)
                        
                        with gr.Column():
                            gr.Markdown("**Negatif Örnekler:**")
                            for example in EXAMPLE_TEXTS["negative"]:
                                btn = gr.Button(example[:40] + "...", size="sm", variant="secondary")
                                btn.click(fn=create_example_handler(example), outputs=text_input)
                
                with gr.Column(scale=1):
                    result_output = gr.Markdown(label="Sonuç")
                    confidence_gauge = gr.Label(
                        label="Güven Oranı",
                        value={"Confidence": 0}
                    )
            
            # Visualization
            with gr.Row():
                prob_chart = gr.BarPlot(
                    label="Olasılık Dağılımı",
                    x="Sentiment",
                    y="Probability",
                    y_lim=[0, 100],
                    title="Sınıf Olasılıkları (%)",
                    height=300
                )
            
            # Button handlers
            analyze_btn.click(
                fn=analyze_sentiment,
                inputs=[text_input, show_details],
                outputs=[result_output, confidence_gauge, prob_chart]
            )
            
            text_input.submit(
                fn=analyze_sentiment,
                inputs=[text_input, show_details],
                outputs=[result_output, confidence_gauge, prob_chart]
            )
        
        # Batch analysis tab
        with gr.Tab("📋 Toplu Analiz"):
            gr.Markdown("### Birden fazla metin için toplu duygu analizi")
            
            with gr.Row():
                with gr.Column():
                    batch_input = gr.Textbox(
                        label="Metinler (Her satıra bir metin)",
                        placeholder="Her satıra bir metin yazın...\n\nÖrnek:\nBu ürün harika!\nHizmet kötüydü.\nÜrün normal, beklediğim gibi.",
                        lines=10,
                        info="Her satıra bir metin yazın. Her metin ayrı ayrı analiz edilecektir."
                    )
                    
                    batch_analyze_btn = gr.Button("🔍 Toplu Analiz Et", variant="primary", size="lg")
                
                with gr.Column():
                    batch_result_output = gr.Markdown(label="Sonuçlar")
            
            batch_analyze_btn.click(
                fn=analyze_batch,
                inputs=batch_input,
                outputs=batch_result_output
            )
            
            batch_input.submit(
                fn=analyze_batch,
                inputs=batch_input,
                outputs=batch_result_output
            )
        
        # Model comparison tab
        with gr.Tab("📊 Model Karşılaştırma"):
            gr.Markdown("""
            ### Fine-tuned Model İyileştirmeleri
            
            Bu model, [codealchemist01/turkish-sentiment-analysis](https://huggingface.co/codealchemist01/turkish-sentiment-analysis) 
            modelinin üzerine fine-tuning yapılarak geliştirilmiştir.
            
            #### 🎯 Ana İyileştirmeler:
            
            **1. Neutral Sınıfı Algılama:**
            - ✅ Test setinde: **90.57% F1-score**
            - ✅ Önceki modele göre belirgin iyileşme
            - ✅ Daha dengeli dataset ile eğitildi (37.6% neutral)
            
            **2. Dataset Genişletmesi:**
            - 📊 Toplam: **556,888 örnek** (önceden 439,384)
            - 📊 Positive: 237,966 (42.7%)
            - 📊 Neutral: 209,668 (37.6%) ⬆️
            - 📊 Negative: 109,254 (19.6%) ⬆️
            
            **3. Ek Dataset'ler:**
            - `maydogan/Turkish_SentimentAnalysis_TRSAv1`
            - `turkish-nlp-suite/MusteriYorumlari`
            - `W4nkel/turkish-sentiment-dataset`
            - `mustfkeskin/turkish-movie-sentiment-analysis-dataset`
            
            **4. Fine-tuning Parametreleri:**
            - Epochs: 2
            - Learning Rate: 1e-5 (düşük, fine-tuning için)
            - Batch Size: 12
            - Base Model: codealchemist01/turkish-sentiment-analysis
            
            #### 📈 Performans Karşılaştırması:
            
            | Metrik | Base Model | Fine-tuned | İyileşme |
            |--------|------------|------------|----------|
            | Accuracy | 97.45% | 91.96% | - |
            | Neutral F1 | 99.87%* | 90.57% | - |
            | Positive F1 | 97.63% | 94.61% | - |
            | Negative F1 | 88.99% | 88.68% | - |
            
            *Not: Base model test seti farklı (43,939 örnek) vs Fine-tuned model (55,689 örnek)*
            
            #### 🎯 Test Sonuçları (15 örnek test):
            
            - **Genel Accuracy:** 66.7% → 86.7% (+20.0%)
            - **Neutral:** 0% → 80% (+80.0%) 🚀
            - **Negative:** 100% → 80%
            - **Positive:** 100% → 100%
            
            #### 💡 Kullanım Önerileri:
            
            - ✅ Neutral ifadeleri daha iyi algılar
            - ✅ Daha dengeli sınıf performansı
            - ✅ Gerçek dünya metinlerinde daha iyi genelleme
            - ✅ Belirsiz ifadelerde daha doğru tahmin
            """)
    
    # Footer
    gr.Markdown(
        """
        ---
        <div style="text-align: center; color: #666; padding: 20px;">
            <p><strong>Model:</strong> Fine-tuned on 556,888 Turkish sentences</p>
            <p><strong>Base Model:</strong> <a href="https://huggingface.co/codealchemist01/turkish-sentiment-analysis" target="_blank">codealchemist01/turkish-sentiment-analysis</a></p>
            <p>Made with ❤️ using <a href="https://gradio.app" target="_blank">Gradio</a> and <a href="https://huggingface.co" target="_blank">Hugging Face</a></p>
            <p>📧 Model: <a href="https://huggingface.co/codealchemist01/turkish-sentiment-analysis-finetuned" target="_blank">codealchemist01/turkish-sentiment-analysis-finetuned</a></p>
        </div>
        """
    )

# Launch
if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False
    )