import gradio as gr
import json
import time
import random
from datetime import datetime
from typing import Dict, Any, List, Optional

# Simulated backend services (in production, these would connect to the actual backend)
class MockNLPEngine:
    """Mock NLP Engine for email analysis"""
    
    def analyze_email(self, subject: str, content: str) -> Dict[str, Any]:
        text = f"{subject} {content}".lower()
        
        # Sentiment analysis
        positive_words = ["good", "great", "excellent", "happy", "love", "thank", "appreciate"]
        negative_words = ["bad", "terrible", "problem", "issue", "error", "failed", "hate", "angry"]
        
        positive_count = sum(1 for word in positive_words if word in text)
        negative_count = sum(1 for word in negative_words if word in text)
        
        if positive_count > negative_count:
            sentiment = "positive"
            sentiment_confidence = min(0.6 + positive_count * 0.1, 0.95)
        elif negative_count > positive_count:
            sentiment = "negative"
            sentiment_confidence = min(0.6 + negative_count * 0.1, 0.95)
        else:
            sentiment = "neutral"
            sentiment_confidence = 0.7
        
        # Topic classification
        topic_patterns = {
            "Work & Business": ["meeting", "project", "deadline", "work", "office", "business", "report"],
            "Personal & Family": ["family", "friend", "personal", "home", "birthday", "vacation"],
            "Finance": ["invoice", "payment", "budget", "money", "bank", "transaction"],
            "Marketing": ["promotion", "offer", "discount", "sale", "newsletter", "subscribe"],
            "Technical": ["bug", "error", "code", "system", "update", "software", "server"]
        }
        
        topic_scores = {}
        for topic, keywords in topic_patterns.items():
            score = sum(1 for kw in keywords if kw in text)
            if score > 0:
                topic_scores[topic] = score
        
        if topic_scores:
            topic = max(topic_scores, key=topic_scores.get)
            topic_confidence = min(0.5 + topic_scores[topic] * 0.15, 0.95)
        else:
            topic = "General"
            topic_confidence = 0.5
        
        # Intent detection
        if "?" in text or any(w in text for w in ["what", "how", "when", "where", "why"]):
            intent = "question"
        elif any(w in text for w in ["please", "could you", "can you", "would you"]):
            intent = "request"
        elif any(w in text for w in ["thank", "thanks", "appreciate"]):
            intent = "gratitude"
        elif any(w in text for w in ["sorry", "apologize", "apologies"]):
            intent = "apology"
        else:
            intent = "informational"
        
        # Urgency detection
        urgent_words = ["urgent", "asap", "immediately", "critical", "emergency", "deadline"]
        urgency = "high" if any(w in text for w in urgent_words) else "normal"
        
        # Keywords extraction
        words = text.split()
        keywords = [w for w in words if len(w) > 4 and w.isalpha()][:10]
        
        return {
            "sentiment": sentiment,
            "sentiment_confidence": sentiment_confidence,
            "topic": topic,
            "topic_confidence": topic_confidence,
            "intent": intent,
            "urgency": urgency,
            "categories": [topic],
            "keywords": keywords,
            "reasoning": f"Email classified as {topic} with {sentiment} sentiment. Intent appears to be {intent}.",
            "suggested_labels": [topic.lower().replace(" & ", "_").replace(" ", "_"), urgency],
            "risk_flags": ["potential_spam"] if "unsubscribe" in text else []
        }

class MockDashboardService:
    """Mock Dashboard Service for statistics"""
    
    def get_stats(self) -> Dict[str, Any]:
        return {
            "total_emails": random.randint(1000, 5000),
            "auto_labeled": random.randint(500, 2000),
            "categories": random.randint(8, 15),
            "time_saved": f"{random.randint(10, 50)}h {random.randint(0, 59)}m",
            "weekly_growth": {
                "emails": random.randint(50, 200),
                "percentage": round(random.uniform(5, 25), 1)
            }
        }

class MockWorkflowEngine:
    """Mock Workflow Engine"""
    
    def __init__(self):
        self.workflows = {
            "default": {"name": "Default Workflow", "description": "Standard email processing", "nodes": 4},
            "ai_enhanced": {"name": "AI Enhanced", "description": "Full AI analysis pipeline", "nodes": 6},
            "quick_filter": {"name": "Quick Filter", "description": "Fast categorization only", "nodes": 2}
        }
    
    def list_workflows(self) -> List[Dict[str, Any]]:
        return [{"id": k, **v} for k, v in self.workflows.items()]
    
    def run_workflow(self, workflow_id: str, email_data: Dict[str, Any]) -> Dict[str, Any]:
        time.sleep(0.5)  # Simulate processing
        return {
            "status": "completed",
            "workflow_id": workflow_id,
            "processed_at": datetime.now().isoformat(),
            "results": {
                "nodes_executed": self.workflows.get(workflow_id, {}).get("nodes", 0),
                "success": True
            }
        }

# Initialize mock services
nlp_engine = MockNLPEngine()
dashboard_service = MockDashboardService()
workflow_engine = MockWorkflowEngine()

#