import requests
from bs4 import BeautifulSoup
import re
from datetime import datetime
import pytz
from PIL import Image
from io import BytesIO
import tempfile
import os
import speech_recognition as sr
from ddgs import DDGS
from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound
from transformers import BlipProcessor, BlipForConditionalGeneration
import torch
# Lazy import pydub to avoid ffmpeg warning when not needed
# from pydub import AudioSegment
# handlers for tools

# 1. get_weather
def get_weather(latitude : str, longitude : str) -> str:
    """Get current temperature for a given location using latitude and longitude coordinates."""
    api = f"https://api.open-meteo.com/v1/forecast?latitude={latitude}&longitude={longitude}&current=temperature_2m,wind_speed_10m&hourly=temperature_2m,relative_humidity_2m,wind_speed_10m"
    response = requests.get(api)
    return response.json()["current"]["temperature_2m"]

# 2. web_search
def web_search(query: str, max_results: int = 5) -> str:
    """Enhanced web search with better error handling"""
    try:
        results_text = []
        with DDGS() as ddgs:
            # Add more specific search parameters
            results = ddgs.text(
                query, 
                max_results=max_results,
                region='us-en',
                safesearch='off'
            )
            
            for i, result in enumerate(results):
                title = result.get("title", "")
                snippet = result.get("body", "")
                link = result.get("href", "")
                
                # Clean up the text
                title = title.strip() if title else ""
                snippet = snippet.strip() if snippet else ""
                
                if title or snippet:
                    results_text.append(f"{i+1}. {title}\n🔗 {link}\n📝 {snippet}\n")
        
        if results_text:
            return "\n".join(results_text)
        else:
            # Try alternative search approach
            return f"No results found for: {query}. Try rephrasing your search query."
            
    except Exception as e:
        return f"Search error: {str(e)}. Please try a different search query."


# 3. get_time_in_location
def get_current_time(timezone: str = "Asia/Kolkata") -> str:
    """Get current time in specified timezone."""
    try:
        # Convert timezone string to pytz timezone object
        tz = pytz.timezone(timezone)
        current_time = datetime.now(tz)
        
        # Format the time nicely
        formatted_time = current_time.strftime("%Y-%m-%d %H:%M:%S %Z")
        
        return f"Current time in {timezone}: {formatted_time}"
        
    except Exception as e:
        return f"Error getting time: {str(e)}"

def get_time_in_location(location: str) -> str:
    """Get current time for a specific location."""
    # Map common locations to timezones
    location_timezones = {
        "gujarat": "Asia/Kolkata",
        "india": "Asia/Kolkata", 
        "mumbai": "Asia/Kolkata",
        "delhi": "Asia/Kolkata",
        "paris": "Europe/Paris",
        "london": "Europe/London",
        "new york": "America/New_York",
        "tokyo": "Asia/Tokyo",
        "sydney": "Australia/Sydney",
        "utc": "UTC"
    }
    
    location_lower = location.lower()
    
    if location_lower in location_timezones:
        timezone = location_timezones[location_lower]
        return get_current_time(timezone)
    else:
        return f"I don't have timezone information for '{location}'. Try: Gujarat, India, Mumbai, Delhi, Paris, London, New York, Tokyo, Sydney, or UTC."
    
# 4. analyze_image
def analyze_image(image_url: str, question: str = None) -> str:
    """Analyze an image and optionally answer a visual question about it."""
    try:
        # Download the image
        headers = {"User-Agent": "Mozilla/5.0"}
        response = requests.get(image_url, headers=headers, timeout=30)
        response.raise_for_status()
        
        # Open image
        image = Image.open(BytesIO(response.content)).convert('RGB')
        
        # Load BLIP model for visual QA or captioning
        processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
        model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-vqa-base")

        device = "cuda" if torch.cuda.is_available() else "cpu"
        model.to(device)

        if question:
            inputs = processor(image, question, return_tensors="pt").to(device)
            out = model.generate(**inputs)
            answer = processor.decode(out[0], skip_special_tokens=True)
            return f"❓ Question: {question}\n🧠 Answer: {answer}"
        else:
            # If no question, generate a caption
            processor_caption = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
            model_caption = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
            model_caption.to(device)

            inputs = processor_caption(image, return_tensors="pt").to(device)
            out = model_caption.generate(**inputs)
            caption = processor_caption.decode(out[0], skip_special_tokens=True)
            return f"🖼️ Image Caption: {caption}"

    except Exception as e:
        return f"Error analyzing image: {e}"

# 5. analyze_video
def analyze_video(video_url: str) -> str:
    """Analyze YouTube video metadata and attempt to fetch subtitles/transcript."""
    try:
        # Validate YouTube URL and extract video ID
        video_id_match = re.search(r'(?:youtube\.com\/(?:watch\?v=|shorts\/)|youtu\.be\/)([^&\n?#\/]+)', video_url)
        if not video_id_match:
            return "Invalid YouTube URL format. Please provide a valid YouTube or Shorts URL."
        
        video_id = video_id_match.group(1)
        headers = {
            "User-Agent": "Mozilla/5.0"
        }

        response = requests.get(video_url, headers=headers, timeout=20)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')

        # Extract title
        title = None
        for selector in ['meta[property="og:title"]', 'meta[name="title"]', 'title']:
            el = soup.select_one(selector)
            if el:
                title = el.get('content') or el.text
                break
        title_text = title.strip() if title else "Unknown title"

        # Extract description
        description = None
        for selector in ['meta[property="og:description"]', 'meta[name="description"]']:
            el = soup.select_one(selector)
            if el:
                description = el.get('content') or el.text
                break
        desc_text = description.strip() if description else "No description available"

        # Extract channel
        channel = None
        for selector in ['link[itemprop="name"]', 'meta[itemprop="channelId"]', 'a[href*="/@"]']:
            el = soup.select_one(selector)
            if el:
                channel = el.get('content') or el.text or el.get('title')
                break
        channel_text = channel.strip() if channel else "Unknown channel"

        # Attempt to fetch transcript using YouTubeTranscriptApi
        transcript_text = "Transcript not available."
        try:
            transcript = YouTubeTranscriptApi.get_transcript(video_id)
            transcript_text = "\n".join([f"{line['text']}" for line in transcript[:5]]) + "\n..."
        except (TranscriptsDisabled, NoTranscriptFound):
            transcript_text = "No transcript available for this video."
        except Exception as e:
            transcript_text = f"Transcript fetch failed: {e}"

        # Final analysis summary
        result = f"📺 Video Analysis\n"
        result += f"Title: {title_text}\n"
        result += f"Channel: {channel_text}\n"
        result += f"Description: {desc_text[:300]}...\n"
        result += f"Transcript Preview:\n{transcript_text}"

        return result

    except Exception as e:
        return f"Error analyzing video: {e}"

# 6. transcribe_audio
def transcribe_audio(audio_url: str) -> str:
    """Transcribe audio content from an audio file URL using speech recognition."""
    try:
        # Download the audio file
        headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"}
        response = requests.get(audio_url, headers=headers, timeout=30)
        response.raise_for_status()
        
        # Create a temporary file to store the audio
        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as temp_audio:
            temp_audio.write(response.content)
            temp_audio_path = temp_audio.name
        
        try:
            # Try to convert audio to WAV format (speech_recognition works better with WAV)
            try:
                # Lazy import pydub only when needed
                from pydub import AudioSegment
                audio = AudioSegment.from_file(temp_audio_path)
                
                # Create temporary WAV file
                with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as temp_wav:
                    audio.export(temp_wav.name, format="wav")
                    wav_path = temp_wav.name
                    
            except Exception as e:
                # If conversion fails, try using the original file directly
                print(f"Audio conversion warning: {e}")
                wav_path = temp_audio_path
            
            # Initialize speech recognizer
            recognizer = sr.Recognizer()
            
            # Load the audio file
            with sr.AudioFile(wav_path) as source:
                # Adjust for ambient noise
                recognizer.adjust_for_ambient_noise(source, duration=0.5)
                # Record the audio
                audio_data = recognizer.record(source)
                
                # Try to recognize speech
                try:
                    # Use Google Speech Recognition (free, requires internet)
                    text = recognizer.recognize_google(audio_data)
                    return f"Audio Transcription:\n{text}"
                    
                except sr.UnknownValueError:
                    return "Audio Transcription: Could not understand the audio. The speech may be unclear, too quiet, or in a language not supported."
                    
                except sr.RequestError as e:
                    return f"Audio Transcription: Error with speech recognition service: {str(e)}"
                    
        finally:
            # Clean up temporary files
            try:
                os.unlink(temp_audio_path)
                if wav_path != temp_audio_path:  # Only delete wav file if it's different
                    os.unlink(wav_path)
            except:
                pass
                
    except ImportError:
        return "Audio Transcription: Speech recognition library not available. Please install 'SpeechRecognition' package."
        
    except Exception as e:
        return f"Audio Transcription Error: {str(e)}"