"""
Google Gemini client for Pip.
Handles: Text generation (emotion analysis, conversation) and image generation.
Uses gemini-flash-lite-latest for text, imagen-4.0-fast-generate-001 for images.
"""

import os
import json
from typing import Optional, AsyncGenerator
import google.generativeai as genai
from google.generativeai import types
import base64


class GeminiClient:
    """
    Gemini-powered client for Pip.
    Primary LLM for emotion analysis, conversation, and image generation.
    """
    
    # Model configurations - using actual available model names
    TEXT_MODEL_FAST = "models/gemini-flash-lite-latest"
    TEXT_MODEL_PRO = "models/gemini-flash-lite-latest"
    IMAGE_MODEL = "models/imagen-4.0-fast-generate-001"
    
    def __init__(self, api_key: str = None):
        """Initialize with optional custom API key."""
        self.api_key = api_key or os.getenv("GOOGLE_API_KEY")
        self.available = bool(self.api_key)
        
        if self.available:
            genai.configure(api_key=self.api_key)
            print(f"✅ Gemini: Configured with model {self.TEXT_MODEL_FAST}")
        else:
            print("⚠️ Gemini: No API key found - service disabled")
        
        # Model instances (lazy loaded)
        self._fast_model = None
        self._pro_model = None
    
    def is_available(self) -> bool:
        """Check if the client is available."""
        return self.available
    
    def _get_fast_model(self):
        """Get fast model for quick responses."""
        if self._fast_model is None:
            self._fast_model = genai.GenerativeModel(self.TEXT_MODEL_FAST)
        return self._fast_model
    
    def _get_pro_model(self):
        """Get pro model for complex reasoning."""
        if self._pro_model is None:
            self._pro_model = genai.GenerativeModel(self.TEXT_MODEL_PRO)
        return self._pro_model
    
    
    async def analyze_emotion(self, user_input: str, system_prompt: str) -> dict:
        """
        Analyze emotional content of user input.
        Returns structured emotion analysis.
        """
        if not self.available:
            return {
                "primary_emotions": ["neutral"],
                "intensity": 5,
                "pip_expression": "neutral",
                "intervention_needed": False
            }
        
        try:
            model = self._get_pro_model()
            
            prompt = f"""{system_prompt}

USER INPUT TO ANALYZE:
{user_input}

Remember: Respond with ONLY valid JSON, no markdown formatting."""

            response = await model.generate_content_async(
                prompt,
                generation_config=types.GenerationConfig(
                    temperature=0.3,
                    max_output_tokens=1024,
                )
            )
            
            result_text = response.text.strip()
            
            # Parse JSON response
            if result_text.startswith("```"):
                result_text = result_text.split("```")[1]
                if result_text.startswith("json"):
                    result_text = result_text[4:]
            
            return json.loads(result_text)
            
        except json.JSONDecodeError as e:
            print(f"Gemini emotion analysis JSON error: {e}")
            return {
                "primary_emotions": ["neutral"],
                "secondary_emotions": [],
                "intensity": 5,
                "underlying_needs": ["connection"],
                "intervention_needed": False
            }
        except Exception as e:
            print(f"Gemini emotion analysis error: {e}")
            raise
    
    async def decide_action(self, emotion_state: dict, system_prompt: str) -> dict:
        """
        Decide what action Pip should take based on emotion analysis.
        """
        try:
            model = self._get_fast_model()
            
            prompt = f"""{system_prompt}

EMOTION ANALYSIS:
{json.dumps(emotion_state, indent=2)}

Respond with ONLY valid JSON, no markdown."""

            response = await model.generate_content_async(
                prompt,
                generation_config=types.GenerationConfig(
                    temperature=0.4,
                    max_output_tokens=512,
                )
            )
            
            result_text = response.text.strip()
            
            if result_text.startswith("```"):
                result_text = result_text.split("```")[1]
                if result_text.startswith("json"):
                    result_text = result_text[4:]
            
            return json.loads(result_text)
            
        except json.JSONDecodeError:
            return {
                "action": "reflect",
                "image_style": "warm",
                "suggested_response_tone": "empathetic"
            }
        except Exception as e:
            print(f"Gemini action decision error: {e}")
            raise
    
    async def quick_acknowledge(self, user_input: str, system_prompt: str) -> str:
        """
        Generate a quick acknowledgment (< 500ms target).
        Uses the fastest available model.
        """
        if not self.available:
            return "I hear you..."
        
        try:
            model = self._get_fast_model()
            
            prompt = f"""{system_prompt}

USER SAID: {user_input}

Respond with JUST the acknowledgment phrase, nothing else."""

            response = await model.generate_content_async(
                prompt,
                generation_config=types.GenerationConfig(
                    temperature=0.7,
                    max_output_tokens=50,
                )
            )
            
            return response.text.strip()
            
        except Exception as e:
            print(f"Gemini quick ack error: {e}")
            return "I hear you..."
    
    async def generate_response_stream(
        self,
        user_input: str,
        emotion_state: dict,
        action: dict,
        system_prompt: str,
        history: list = None
    ) -> AsyncGenerator[str, None]:
        """
        Generate conversational response with streaming.
        """
        try:
            model = self._get_pro_model()
            
            # Build context
            history_text = ""
            if history:
                history_text = "\n".join([
                    f"{m['role'].upper()}: {m['content']}" 
                    for m in history[-6:]
                ])
            
            prompt = f"""{system_prompt}

EMOTION ANALYSIS:
{json.dumps(emotion_state, indent=2)}

ACTION TO TAKE:
{json.dumps(action, indent=2)}

CONVERSATION HISTORY:
{history_text}

CURRENT USER MESSAGE:
{user_input}

Respond naturally and warmly. Be concise but meaningful."""

            response = await model.generate_content_async(
                prompt,
                generation_config=types.GenerationConfig(
                    temperature=0.8,
                    max_output_tokens=500,
                ),
                stream=True
            )
            
            async for chunk in response:
                if chunk.text:
                    yield chunk.text
                    
        except Exception as e:
            print(f"Gemini response stream error: {e}")
            yield "I'm here with you. Tell me more about what's on your mind."
    
    async def generate_intervention_response(
        self,
        user_input: str,
        emotion_state: dict,
        system_prompt: str
    ) -> AsyncGenerator[str, None]:
        """
        Generate careful intervention response for concerning situations.
        """
        try:
            model = self._get_pro_model()
            
            prompt = f"""{system_prompt}

USER INPUT: {user_input}

EMOTION ANALYSIS:
{json.dumps(emotion_state, indent=2)}

Respond with care, warmth, and appropriate resources if needed."""

            response = await model.generate_content_async(
                prompt,
                generation_config=types.GenerationConfig(
                    temperature=0.5,
                    max_output_tokens=600,
                ),
                stream=True
            )
            
            async for chunk in response:
                if chunk.text:
                    yield chunk.text
                    
        except Exception as e:
            print(f"Gemini intervention error: {e}")
            yield "I hear that you're going through something difficult. I'm here with you, and I care about how you're feeling."
    
    async def generate_text(self, prompt: str) -> Optional[str]:
        """
        Generate text (for prompts, summaries, etc).
        """
        if not self.available:
            return None
        
        try:
            model = self._get_pro_model()
            response = await model.generate_content_async(
                prompt,
                generation_config=types.GenerationConfig(
                    temperature=0.7,
                    max_output_tokens=1024,
                )
            )
            return response.text
        except Exception as e:
            print(f"Gemini text generation error: {e}")
            return None
    
    async def enhance_prompt(
        self,
        user_input: str,
        emotion_state: dict,
        mode: str,
        system_prompt: str
    ) -> str:
        """
        Enhance a prompt for image generation.
        """
        try:
            model = self._get_fast_model()
            
            prompt = f"""{system_prompt}

USER INPUT: {user_input}
EMOTIONS: {json.dumps(emotion_state.get('primary_emotions', []))}
MODE: {mode}

Generate the enhanced image prompt only, no explanation."""

            response = await model.generate_content_async(
                prompt,
                generation_config=types.GenerationConfig(
                    temperature=0.9,
                    max_output_tokens=200,
                )
            )
            
            return response.text.strip()
            
        except Exception as e:
            print(f"Gemini prompt enhancement error: {e}")
            return f"A peaceful scene reflecting {emotion_state.get('primary_emotions', ['calm'])[0]}"
    
    async def generate_image(self, prompt: str) -> Optional[str]:
        """
        Generate an image using Imagen 4.0.
        Returns base64 encoded image or None.
        """
        if not self.available:
            return None
        
        try:
            # Use GenerativeModel with Imagen 4.0
            imagen_model = genai.GenerativeModel(self.IMAGE_MODEL)
            
            response = await imagen_model.generate_content_async(
                prompt,
                generation_config=types.GenerationConfig(
                    temperature=1.0,
                )
            )
            
            # Check for image in response
            if response.candidates:
                for candidate in response.candidates:
                    if hasattr(candidate, 'content') and candidate.content.parts:
                        for part in candidate.content.parts:
                            if hasattr(part, 'inline_data') and part.inline_data:
                                # Return base64 encoded image
                                return base64.b64encode(part.inline_data.data).decode('utf-8')
            
            print("Imagen: No image in response")
            return None
            
        except Exception as e:
            error_str = str(e)
            if "429" in error_str or "quota" in error_str.lower():
                print(f"Imagen rate limit exceeded: {e}")
            elif "not found" in error_str.lower() or "not supported" in error_str.lower():
                print(f"Imagen not available: {e}")
                return None
            else:
                print(f"Imagen generation error: {e}")
            return None