"""
Gradio web interface for sentiment analysis.

This module provides a modern, responsive web interface using Gradio
for human interaction with the sentiment analysis system, including
real-time analysis, confidence visualization, and history tracking.
"""

import asyncio
import logging
import json
import os
from typing import Dict, Any, List, Tuple, Optional
from datetime import datetime
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px

try:
    import gradio as gr
    GRADIO_AVAILABLE = True
except ImportError:
    GRADIO_AVAILABLE = False
    logging.error("Gradio not available. Install with: pip install gradio")

from .sentiment_analyzer import get_analyzer, SentimentResult, SentimentLabel
from .tools import list_tools

class SentimentHistory:
    """Manages sentiment analysis history."""
    
    def __init__(self, max_entries: int = 100):
        self.max_entries = max_entries
        self.entries: List[Dict[str, Any]] = []
        self.logger = logging.getLogger(__name__)
    
    def add_entry(self, text: str, result: SentimentResult, backend: str) -> None:
        entry = {
            "timestamp": datetime.now().isoformat(),
            "text": text[:100] + "..." if len(text) > 100 else text,
            "full_text": text,
            "label": result.label.value,
            "confidence": result.confidence,
            "backend": backend,
            "raw_scores": result.raw_scores
        }
        
        self.entries.append(entry)
        
        if len(self.entries) > self.max_entries:
            self.entries = self.entries[-self.max_entries:]
    
    def get_recent_entries(self, count: int = 10) -> List[Dict[str, Any]]:
        return self.entries[-count:] if self.entries else []
    
    def get_statistics(self) -> Dict[str, Any]:
        if not self.entries:
            return {
                "total_analyses": 0,
                "label_distribution": {},
                "average_confidence": 0.0,
                "backend_usage": {}
            }
        
        labels = [entry["label"] for entry in self.entries]
        confidences = [entry["confidence"] for entry in self.entries]
        backends = [entry["backend"] for entry in self.entries]
        
        label_counts = {
            "positive": labels.count("positive"),
            "negative": labels.count("negative"),
            "neutral": labels.count("neutral")
        }
        
        backend_counts = {}
        for backend in backends:
            backend_counts[backend] = backend_counts.get(backend, 0) + 1
        
        return {
            "total_analyses": len(self.entries),
            "label_distribution": label_counts,
            "average_confidence": sum(confidences) / len(confidences),
            "backend_usage": backend_counts
        }


class GradioInterface:
    """Gradio web interface for sentiment analysis."""
    
    def __init__(self, title: str = "Sentiment Analysis Server", 
                 description: str = "Analyze text sentiment using TextBlob or Transformers"):
        self.title = title
        self.description = description
        self.logger = logging.getLogger(__name__)
        self.history = SentimentHistory()
        self.interface = None
        self._setup_interface()
    
    def _setup_interface(self) -> None:
        if not GRADIO_AVAILABLE:
            raise RuntimeError("Gradio not available")
        
        with gr.Blocks(
            theme=gr.themes.Soft(),
            title=self.title
        ) as interface:
            
            gr.Markdown(f"# {self.title}")
            gr.Markdown(f"*{self.description}*")
            
            with gr.Tabs():
                with gr.TabItem("Sentiment Analysis"):
                    with gr.Row():
                        with gr.Column(scale=2):
                            text_input = gr.Textbox(
                                label="Text to Analyze",
                                placeholder="Enter text here to analyze its sentiment...",
                                lines=4
                            )
                            
                            with gr.Row():
                                backend_choice = gr.Dropdown(
                                    choices=["auto", "textblob", "transformers"],
                                    value="auto",
                                    label="Analysis Backend"
                                )
                                
                                analyze_btn = gr.Button(
                                    "Analyze Sentiment",
                                    variant="primary"
                                )
                        
                        with gr.Column(scale=1):
                            result_display = gr.HTML(
                                value="<p>Enter text and click 'Analyze Sentiment' to see results.</p>"
                            )
                            
                            confidence_plot = gr.Plot(visible=False)
                    
                    gr.Markdown("### Quick Examples")
                    with gr.Row():
                        pos_btn = gr.Button("😊 Positive", size="sm")
                        neu_btn = gr.Button("😐 Neutral", size="sm")
                        neg_btn = gr.Button("😞 Negative", size="sm")
                        mix_btn = gr.Button("📝 Mixed", size="sm")
                
                with gr.TabItem("Batch Analysis"):
                    with gr.Row():
                        with gr.Column():
                            batch_input = gr.Textbox(
                                label="Texts to Analyze (one per line)",
                                placeholder="Enter multiple texts, one per line...",
                                lines=8
                            )
                            
                            with gr.Row():
                                batch_backend = gr.Dropdown(
                                    choices=["auto", "textblob", "transformers"],
                                    value="auto",
                                    label="Analysis Backend"
                                )
                                
                                batch_analyze_btn = gr.Button(
                                    "Analyze Batch",
                                    variant="primary"
                                )
                        
                        with gr.Column():
                            batch_results = gr.DataFrame(
                                label="Batch Results",
                                headers=["Text", "Sentiment", "Confidence"]
                            )
                            
                            batch_summary_plot = gr.Plot(visible=False)
                
                with gr.TabItem("Analysis History"):
                    with gr.Row():
                        refresh_history_btn = gr.Button("Refresh History", variant="secondary")
                        clear_history_btn = gr.Button("Clear History", variant="stop")
                    
                    with gr.Row():
                        with gr.Column(scale=2):
                            history_table = gr.DataFrame(
                                label="Recent Analyses",
                                headers=["Time", "Text", "Sentiment", "Confidence", "Backend"]
                            )
                        
                        with gr.Column(scale=1):
                            stats_display = gr.HTML(value="<p>No analyses yet.</p>")
                            history_plot = gr.Plot(visible=False)
                
                with gr.TabItem("Settings & Info"):
                    with gr.Row():
                        with gr.Column():
                            gr.Markdown("### Backend Information")
                            backend_info = gr.HTML(value="<p>Loading backend information...</p>")
                            refresh_info_btn = gr.Button("Refresh Info", variant="secondary")
                        
                        with gr.Column():
                            gr.Markdown("### Usage Tips")
                            gr.Markdown("""
                            - **Auto**: Automatically selects the best available backend
                            - **TextBlob**: Fast, simple sentiment analysis
                            - **Transformers**: More accurate, AI-powered analysis
                            - **Batch Analysis**: Process multiple texts at once
                            - **History**: Track your analysis results over time
                            """)
            
            # Event handlers
            def analyze_sentiment(text: str, backend: str) -> Tuple[str, gr.Plot]:
                return asyncio.run(self._analyze_sentiment_async(text, backend))
            
            def analyze_batch(texts: str, backend: str) -> Tuple[pd.DataFrame, gr.Plot]:
                return asyncio.run(self._analyze_batch_async(texts, backend))
            
            def refresh_history() -> Tuple[pd.DataFrame, str, gr.Plot]:
                return self._get_history_data()
            
            def clear_history() -> Tuple[pd.DataFrame, str, gr.Plot]:
                self.history.entries.clear()
                return self._get_history_data()
            
            def get_backend_info() -> str:
                return asyncio.run(self._get_backend_info_async())
            
            def get_mcp_schema() -> str:
                """Get MCP tools schema as JSON."""
                return asyncio.run(self._get_mcp_schema_async())
            
            # Example texts
            examples = [
                "I absolutely love this new feature! It's incredible and makes everything so much easier.",
                "The weather is okay today, nothing particularly special about it.",
                "This is terrible and frustrating. I hate how complicated this has become.",
                "The movie had great visuals but the plot was disappointing. Mixed feelings overall."
            ]
            
            # Wire up events
            analyze_btn.click(
                analyze_sentiment,
                inputs=[text_input, backend_choice],
                outputs=[result_display, confidence_plot]
            )
            
            batch_analyze_btn.click(
                analyze_batch,
                inputs=[batch_input, batch_backend],
                outputs=[batch_results, batch_summary_plot]
            )
            
            refresh_history_btn.click(
                refresh_history,
                outputs=[history_table, stats_display, history_plot]
            )
            
            clear_history_btn.click(
                clear_history,
                outputs=[history_table, stats_display, history_plot]
            )
            
            refresh_info_btn.click(
                get_backend_info,
                outputs=[backend_info]
            )
            
            # Example buttons
            pos_btn.click(lambda: examples[0], outputs=[text_input])
            neu_btn.click(lambda: examples[1], outputs=[text_input])
            neg_btn.click(lambda: examples[2], outputs=[text_input])
            mix_btn.click(lambda: examples[3], outputs=[text_input])
            
            # Load initial data
            interface.load(get_backend_info, outputs=[backend_info])
            interface.load(refresh_history, outputs=[history_table, stats_display, history_plot])
        
        self.interface = interface
    
    async def _analyze_sentiment_async(self, text: str, backend: str) -> Tuple[str, gr.Plot]:
        try:
            if not text.strip():
                return "<p>Please enter some text to analyze.</p>", gr.Plot(visible=False)
            
            analyzer = await get_analyzer(backend)
            result = await analyzer.analyze(text)
            
            self.history.add_entry(text, result, analyzer.backend)
            
            sentiment_class = f"sentiment-{result.label.value}"
            confidence_class = (
                "confidence-high" if result.confidence > 0.7 
                else "confidence-medium" if result.confidence > 0.4 
                else "confidence-low"
            )
            
            html_result = f"""
            <div style="padding: 1rem; border-radius: 0.5rem; background: #f8fafc; border-left: 4px solid #3b82f6;">
                <h3>Analysis Result</h3>
                <p><strong>Sentiment:</strong> <span style="color: {'#22c55e' if result.label.value == 'positive' else '#ef4444' if result.label.value == 'negative' else '#6b7280'}; font-weight: bold;">{result.label.value.title()}</span></p>
                <p><strong>Confidence:</strong> <span style="color: {'#059669' if result.confidence > 0.7 else '#d97706' if result.confidence > 0.4 else '#dc2626'};">{result.confidence:.2%}</span></p>
                <p><strong>Backend:</strong> {analyzer.backend}</p>
                <p><strong>Text Length:</strong> {len(text)} characters</p>
            </div>
            """
            
            plot = self._create_confidence_plot(result)
            return html_result, plot
            
        except Exception as e:
            self.logger.error(f"Analysis failed: {e}")
            error_html = f"""
            <div style="padding: 1rem; border-radius: 0.5rem; background: #fef2f2; border-left: 4px solid #ef4444;">
                <h3>Analysis Error</h3>
                <p><strong>Error:</strong> {str(e)}</p>
                <p>Please try again or check your input.</p>
            </div>
            """
            return error_html, gr.Plot(visible=False)
    
    async def _analyze_batch_async(self, texts: str, backend: str) -> Tuple[pd.DataFrame, gr.Plot]:
        try:
            if not texts.strip():
                return pd.DataFrame(), gr.Plot(visible=False)
            
            text_list = [t.strip() for t in texts.split('\n') if t.strip()]
            
            if not text_list:
                return pd.DataFrame(), gr.Plot(visible=False)
            
            analyzer = await get_analyzer(backend)
            results = await analyzer.analyze_batch(text_list)
            
            data = []
            for text, result in zip(text_list, results):
                self.history.add_entry(text, result, analyzer.backend)
                
                data.append({
                    "Text": text[:50] + "..." if len(text) > 50 else text,
                    "Sentiment": result.label.value.title(),
                    "Confidence": f"{result.confidence:.2%}"
                })
            
            df = pd.DataFrame(data)
            plot = self._create_batch_summary_plot(results)
            
            return df, plot
            
        except Exception as e:
            self.logger.error(f"Batch analysis failed: {e}")
            return pd.DataFrame([{"Error": str(e)}]), gr.Plot(visible=False)
    
    def _create_confidence_plot(self, result: SentimentResult) -> gr.Plot:
        try:
            fig = go.Figure(go.Indicator(
                mode="gauge+number",
                value=result.confidence * 100,
                domain={'x': [0, 1], 'y': [0, 1]},
                title={'text': f"Confidence - {result.label.value.title()}"},
                gauge={
                    'axis': {'range': [None, 100]},
                    'bar': {'color': "darkblue"},
                    'steps': [
                        {'range': [0, 40], 'color': "lightgray"},
                        {'range': [40, 70], 'color': "yellow"},
                        {'range': [70, 100], 'color': "green"}
                    ]
                }
            ))
            
            fig.update_layout(height=300, margin=dict(l=20, r=20, t=40, b=20))
            return gr.Plot(value=fig, visible=True)
            
        except Exception as e:
            self.logger.error(f"Failed to create confidence plot: {e}")
            return gr.Plot(visible=False)
    
    def _create_batch_summary_plot(self, results: List[SentimentResult]) -> gr.Plot:
        try:
            labels = [result.label.value for result in results]
            label_counts = {
                "Positive": labels.count("positive"),
                "Negative": labels.count("negative"),
                "Neutral": labels.count("neutral")
            }
            
            fig = px.pie(
                values=list(label_counts.values()),
                names=list(label_counts.keys()),
                title="Sentiment Distribution",
                color_discrete_map={
                    "Positive": "#22c55e",
                    "Negative": "#ef4444",
                    "Neutral": "#6b7280"
                }
            )
            
            fig.update_layout(height=300, margin=dict(l=20, r=20, t=40, b=20))
            return gr.Plot(value=fig, visible=True)
            
        except Exception as e:
            self.logger.error(f"Failed to create batch summary plot: {e}")
            return gr.Plot(visible=False)
    
    def _get_history_data(self) -> Tuple[pd.DataFrame, str, gr.Plot]:
        try:
            entries = self.history.get_recent_entries(20)
            
            if not entries:
                empty_df = pd.DataFrame(columns=["Time", "Text", "Sentiment", "Confidence", "Backend"])
                return empty_df, "<p>No analyses yet.</p>", gr.Plot(visible=False)
            
            data = []
            for entry in reversed(entries):
                data.append({
                    "Time": entry["timestamp"][:19].replace("T", " "),
                    "Text": entry["text"],
                    "Sentiment": entry["label"].title(),
                    "Confidence": f"{entry['confidence']:.2%}",
                    "Backend": entry["backend"]
                })
            
            df = pd.DataFrame(data)
            stats = self.history.get_statistics()
            
            stats_html = f"""
            <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 1rem; border-radius: 0.5rem;">
                <h3>📊 Analysis Statistics</h3>
                <p><strong>Total Analyses:</strong> {stats['total_analyses']}</p>
                <p><strong>Average Confidence:</strong> {stats['average_confidence']:.2%}</p>
                <h4>Sentiment Distribution:</h4>
                <ul>
                    <li>Positive: {stats['label_distribution'].get('positive', 0)}</li>
                    <li>Negative: {stats['label_distribution'].get('negative', 0)}</li>
                    <li>Neutral: {stats['label_distribution'].get('neutral', 0)}</li>
                </ul>
            </div>
            """
            
            plot = self._create_history_plot(stats) if stats['total_analyses'] > 0 else gr.Plot(visible=False)
            return df, stats_html, plot
            
        except Exception as e:
            self.logger.error(f"Failed to get history data: {e}")
            error_df = pd.DataFrame([{"Error": str(e)}])
            return error_df, f"<p>Error loading history: {e}</p>", gr.Plot(visible=False)
    
    def _create_history_plot(self, stats: Dict[str, Any]) -> gr.Plot:
        try:
            labels = list(stats['label_distribution'].keys())
            values = list(stats['label_distribution'].values())
            
            fig = px.bar(
                x=[label.title() for label in labels],
                y=values,
                title="Historical Sentiment Distribution",
                color=labels,
                color_discrete_map={
                    "positive": "#22c55e",
                    "negative": "#ef4444",
                    "neutral": "#6b7280"
                }
            )
            
            fig.update_layout(height=300, margin=dict(l=20, r=20, t=40, b=20), showlegend=False)
            return gr.Plot(value=fig, visible=True)
            
        except Exception as e:
            self.logger.error(f"Failed to create history plot: {e}")
            return gr.Plot(visible=False)
    
    async def _get_backend_info_async(self) -> str:
        try:
            analyzer = await get_analyzer("auto")
            info = analyzer.get_info()
            
            html = f"""
            <div style="padding: 1rem; border-radius: 0.5rem; background: #f0f9ff; border-left: 4px solid #0ea5e9;">
                <h3>🔧 Backend Information</h3>
                <p><strong>Current Backend:</strong> {info['backend']}</p>
                <p><strong>Model Loaded:</strong> {'Yes' if info['model_loaded'] else 'No'}</p>
                <p><strong>TextBlob Available:</strong> {'Yes' if info['textblob_available'] else 'No'}</p>
                <p><strong>Transformers Available:</strong> {'Yes' if info['transformers_available'] else 'No'}</p>
                <p><strong>CUDA Available:</strong> {'Yes' if info.get('cuda_available', False) else 'No'}</p>
                {f"<p><strong>Model Name:</strong> {info['model_name']}</p>" if info.get('model_name') else ""}
            </div>
            """
            return html
            
        except Exception as e:
            self.logger.error(f"Failed to get backend info: {e}")
            return f"""
            <div style="padding: 1rem; border-radius: 0.5rem; background: #fef2f2; border-left: 4px solid #ef4444;">
                <h3>❌ Backend Error</h3>
                <p>Failed to load backend information: {str(e)}</p>
            </div>
            """
    
    async def _get_mcp_schema_async(self) -> str:
        """Get MCP tools schema as formatted JSON."""
        try:
            tools = await list_tools()
            schema = {
                "mcp_version": "2024-11-05",
                "server_info": {
                    "name": "sentiment-analyzer",
                    "version": "1.0.0",
                    "description": "Sentiment analysis server using TextBlob and Transformers"
                },
                "tools": tools,
                "total_tools": len(tools)
            }
            return json.dumps(schema, indent=2)
            
        except Exception as e:
            self.logger.error(f"Failed to get MCP schema: {e}")
            return json.dumps({
                "error": str(e),
                "error_type": type(e).__name__
            }, indent=2)
    
    def launch(self, **kwargs) -> None:
        if not self.interface:
            raise RuntimeError("Interface not initialized")
        
        # Check for MCP server mode from environment variable or parameter
        mcp_server_enabled = (
            kwargs.get("mcp_server", False) or 
            os.getenv("GRADIO_MCP_SERVER", "").lower() in ("true", "1", "yes", "on")
        )
        
        launch_params = {
            "server_name": "0.0.0.0",
            "server_port": 7860,
            "share": False,
            "debug": False,
            "show_error": True,
            "quiet": False
        }
        
        # Add MCP server parameter if enabled
        if mcp_server_enabled:
            launch_params["mcp_server"] = True
            self.logger.info("MCP server functionality enabled for Gradio interface")
        
        launch_params.update(kwargs)
        
        self.logger.info(f"Launching Gradio interface on {launch_params['server_name']}:{launch_params['server_port']}")
        if mcp_server_enabled:
            self.logger.info("Gradio interface will also serve as MCP server with API endpoints")
        
        try:
            self.interface.launch(**launch_params)
        except Exception as e:
            self.logger.error(f"Failed to launch interface: {e}")
            raise


def create_gradio_interface(**kwargs) -> GradioInterface:
    if not GRADIO_AVAILABLE:
        raise RuntimeError("Gradio not available. Install with: pip install gradio")
    
    return GradioInterface(**kwargs)


async def main() -> None:
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
    )
    
    interface = create_gradio_interface()
    interface.launch(debug=True)


if __name__ == "__main__":
    asyncio.run(main())