# analyzer.py (Key Point Extraction)
from transformers import pipeline
import re
from datetime import datetime, timedelta
import config

# Load NLP model
summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum")

class MeetingAnalyzer:
    def __init__(self):
        self.transcript_chunks = []
        self.speakers = {}
        self.current_speaker = "Unknown"
        self.action_items = []
        self.decisions = []
        
    def process_chunk(self, text_chunk):
        self.transcript_chunks.append(text_chunk)
        
        # Simple speaker detection
        if ":" in text_chunk:
            speaker, content = text_chunk.split(":", 1)
            self.current_speaker = speaker.strip()
            if self.current_speaker not in self.speakers:
                self.speakers[self.current_speaker] = []
            self.speakers[self.current_speaker].append(content.strip())
    
    def generate_summary(self):
        full_text = " ".join(self.transcript_chunks)
        
        # If text is too short, skip summarization
        if len(full_text.split()) < 50:
            return "Not enough content for summary"
            
        # Generate summary in chunks for long meetings
        max_chunk_size = 1000
        chunks = [full_text[i:i+max_chunk_size] for i in range(0, len(full_text), max_chunk_size)]
        
        summaries = []
        for chunk in chunks:
            summary = summarizer(
                chunk,
                max_length=config.SUMMARY_MAX_LENGTH,
                min_length=config.SUMMARY_MIN_LENGTH,
                do_sample=False
            )[0]['summary_text']
            summaries.append(summary)
            
        return " ".join(summaries)
    
    def extract_action_items(self):
        full_text = " ".join(self.transcript_chunks)
        action_items = []
        
        # Pattern matching for action items
        patterns = [
            r"(\bwill\b.*?\bby\b\s+\w+\s+\d{1,2})",
            r"(\baction\b:\s*(.*?)(?:\bdeadline\b|\bby\b|\bfor\b)\s*(\w+\s+\d{1,2}))",
            r"(\btodo\b:\s*(.*?)(?:\bdue\b|\bby\b)\s*(\w+\s+\d{1,2}))",
            r"(\bassign(?:ed)? to\b\s+(\w+):\s*(.*?)(?:\bdeadline\b|\bby\b)\s*(\w+\s+\d{1,2}))"
        ]
        
        for pattern in patterns:
            for match in re.finditer(pattern, full_text, re.IGNORECASE):
                groups = match.groups()
                if groups:
                    # Different patterns have different group structures
                    if len(groups) == 1:
                        task = groups[0]
                        owner = "Unassigned"
                        deadline = "ASAP"
                    elif len(groups) == 3:
                        task = groups[1]
                        owner = groups[0]
                        deadline = groups[2]
                    else:
                        task = groups[0]
                        owner = "Unassigned"
                        deadline = "ASAP"
                    
                    action_items.append({
                        "task": task.strip(),
                        "owner": owner.strip(),
                        "deadline": self.normalize_deadline(deadline.strip())
                    })
        
        return action_items
    
    def detect_urgent_action_items(self):
        urgent_items = []
        for item in self.action_items:
            if "urgent" in item['task'].lower() or "asap" in item['deadline'].lower():
                urgent_items.append(item)
        return urgent_items
    
    def extract_decisions(self):
        full_text = " ".join(self.transcript_chunks)
        decisions = []
        
        # Pattern matching for decisions
        patterns = [
            r"\bdecided to\b (.*?)[\.\n]",
            r"\bagreed that\b (.*?)[\.\n]",
            r"\bconsensus is\b (.*?)[\.\n]",
            r"\bresolution\b: (.*?)[\.\n]"
        ]
        
        for pattern in patterns:
            for match in re.finditer(pattern, full_text, re.IGNORECASE):
                decision = match.group(1).strip()
                decisions.append(decision)
        
        return decisions
    
    def normalize_deadline(self, deadline_str):
        today = datetime.now()
        lower_str = deadline_str.lower()
        
        if "today" in lower_str:
            return today.strftime("%Y-%m-%d")
        elif "tomorrow" in lower_str:
            return (today + timedelta(days=1)).strftime("%Y-%m-%d")
        elif "next week" in lower_str:
            return (today + timedelta(weeks=1)).strftime("%Y-%m-%d")
        elif "eod" in lower_str:
            return today.strftime("%Y-%m-%d")
        elif "eow" in lower_str:
            # Find next Friday
            days_ahead = 4 - today.weekday()  # 0 = Monday, 4 = Friday
            if days_ahead <= 0:  # If today is Friday or weekend
                days_ahead += 7
            return (today + timedelta(days=days_ahead)).strftime("%Y-%m-%d")
        
        return deadline_str