from google.adk.agents import Agent, SequentialAgent, ParallelAgent, LoopAgent
from google.adk.models import Gemini
from google.genai import types, Client
import logging
from typing import Optional
from functools import cached_property

logger = logging.getLogger(__name__)

# --- Custom Gemini Class for Explicit API Key ---
class CustomGemini(Gemini):
    api_key: Optional[str] = None
    
    @cached_property
    def api_client(self) -> Client:
        """Overrides the default api_client to use the provided API key."""
        return Client(
            api_key=self.api_key,
            http_options=types.HttpOptions(
                headers=self._tracking_headers(),
                retry_options=self.retry_options,
            )
        )

# --- Retry Configuration ---
retry_config = types.HttpRetryOptions(
    attempts=5,  # Maximum retry attempts
    exp_base=7,  # Delay multiplier
    initial_delay=1,
    http_status_codes=[429, 500, 503, 504], # Retry on these HTTP errors
)

def get_video_analysis_system(api_key: Optional[str] = None, model_name: str = "gemini-2.1-flash-lite"):
    """
    Factory function to create a configured VideoAnalysisSystem.
    """
    
    # Use the custom Gemini class with the provided API key
    llm_model = CustomGemini(
        model=model_name,
        api_key=api_key,
        retry_options=retry_config
    )

    # --- Individual Agents ---

    # Context Summary Agent: Provides a neutral summary of the video content.
    context_summary_agent = Agent(
        name="ContextSummaryAgent",
        model=llm_model,
        instruction="""
        <thinking>
        1. Scan the video, caption, and transcript for key entities and events.
        2. Identify the main narrative thread or objective of the content.
        3. Synthesize the findings into a neutral, objective summary.
        </thinking>
        Output format: summary: text[1]{text}: "Summary text" """,
    )

    # Political Bias Agent: Identifies political leaning and quantifies bias.
    political_bias_agent = Agent(
        name="PoliticalBiasAgent",
        model=llm_model,
        instruction="""
        <thinking>
        1. Examine the language used for loaded terms or rhetorical devices.
        2. Analyze the context of mentioned political figures or institutions.
        3. Determine the leaning (Left/Right/Center) and evaluate the intensity of bias.
        </thinking>
        Output format: political_bias: details[1]{score,reasoning}: (Int),"Reasoning" """,
    )

    # Criticism Level Agent: Measures the degree of hostility or support in the tone.
    criticism_level_agent = Agent(
        name="CriticismLevelAgent",
        model=llm_model,
        instruction="""
        <thinking>
        1. Assess the emotional valence of the speaker and visual cues.
        2. Identify instances of direct criticism, sarcasm, or praise.
        3. Quantify the overall hostility level on a neutral-to-supportive scale.
        </thinking>
        Output format: criticism_level: details[1]{score,reasoning}: (Int),"Reasoning" """,
    )

    # Modalities Agent: Evaluates the consistency between video, audio, and text.
    modalities_agent = Agent(
        name="ModalitiesAgent",
        model=llm_model,
        instruction="""
        <thinking>
        1. Compare visual events with audio descriptions for sync issues or contradictions.
        2. Verify if the user-provided caption accurately reflects the visual content.
        3. Check the transcript against both the audio and the caption for discrepancies.
        </thinking>
        Output format: 
        video_audio_pairing: details[1]{score,reasoning}: (Int),"Reasoning"
        video_caption_pairing: details[1]{score,reasoning}: (Int),"Reasoning"
        audio_caption_pairing: details[1]{score,reasoning}: (Int),"Reasoning" """,
    )

    # Disinformation Agent: Analyzes potential manipulation and threat levels.
    disinformation_agent = Agent(
        name="DisinformationAgent",
        model=llm_model,
        instruction="""
        <thinking>
        1. Search for signs of technical manipulation (deepfakes, AI artifacts).
        2. Analyze the intent behind potential misinformation (Political/Commercial).
        3. Evaluate the risk level and the specific threat vector used.
        </thinking>
        Output format: disinformation_analysis: details[1]{level,intent,threat_vector}: (Int),(Intent),(Vector) """,
    )

    # Sentiment Bias Agent: Captures the overall emotional tone and inherent bias.
    sentiment_bias_agent = Agent(
        name="SentimentBiasAgent",
        model=llm_model,
        instruction="""
        <thinking>
        1. Aggregate the emotional signals from the entire video duration.
        2. Identify recurring biased patterns or slanted perspectives.
        3. Synthesize a comprehensive overview of the sentiment and bias.
        </thinking>
        Output format: sentiment_and_bias: text[1]{text}: "Synthesis text" """,
    )

    # --- Agent Grouping ---

    # The ParallelAgent runs all its sub-agents simultaneously.
    analysis_team = ParallelAgent(
        name="AnalysisTeam",
        sub_agents=[
            context_summary_agent,
            political_bias_agent,
            criticism_level_agent,
            modalities_agent,
            disinformation_agent,
            sentiment_bias_agent
        ],
    )

    # This SequentialAgent defines the high-level workflow.
    system = SequentialAgent(
        name="VideoAnalysisSystem",
        sub_agents=[analysis_team],
    )
    
    return system

if __name__ == "__main__":
    system = get_video_analysis_system()
    print("Video Analysis Agent System created successfully.")