# SageAuditory Component: Auditory Processing AGI

class SageAuditory:
    def __init__(self):
        self.capabilities = 16210  # Inherited from EDEN's internal AI functions
        self.sage_count = 4259     # SAGEs created by the business
        self.market_research_cycles = 1017  # Market research cycles conducted

    def initialize(self):
        print("Initializing SageAuditory...")
        self.load_audio_model()
        self.setup_speech_recognition()

    def load_audio_model(self):
        """Load pre-trained audio processing model."""
        print("Loading pre-trained audio processing model...")

        # Hypothetical API call to a cloud service for loading the model
        print("Model loaded successfully.")
        return True

    def setup_speech_recognition(self):
        """Set up speech recognition system using trained models and SAGEs."""
        print("Setting up speech recognition...")
        self.sage_count += 10  # Assuming each new Sage increases capability by 10
        self.capabilities += 50  # Additional capabilities added for the AGI

        print(f"SAGE count increased to: {self.sage_count}")
        print(f"Capabilities increased to: {self.capabilities}")

    def process_audio(self, audio_data):
        """Process incoming audio data using SageAuditory's capabilities."""
        if not self.load_audio_model():
            return "Model not loaded."

        print("Processing audio data...")
        # Hypothetical processing of audio data
        processed_data = self.analyze_speech(audio_data)
        return processed_data

    def analyze_speech(self, speech_data):
        """Analyze the provided speech data."""
        print("Analyzing speech data...")
        # Simulating analysis using SAGEs and internal AI functions
        if "market" in speech_data.lower():
            market_research_results = self.fetch_market_research()
            return f"Market research results: {market_research_results}"
        elif "code review" in speech_data.lower():
            code_review_results = self.run_code_reviews(speech_data)
            return f"Code review results: {code_review_results}"
        else:
            return "No relevant information found."

    def fetch_market_research(self):
        """Fetch market research data based on query."""
        print("Fetching market research data...")
        # Hypothetical API call to a database or service
        return "Example market research findings"

    def run_code_reviews(self, speech_data):
        """Run code reviews as described in the speech data."""
        print("Running code reviews...")
        # Simulating code review using SAGEs and internal AI functions
        if "bug" in speech_data.lower():
            bug_found = True  # Hypothetical detection of a bug
            return f"A potential bug has been identified: {bug_found}"
        else:
            return "No specific code changes requested."

    def summarize_findings(self):
        """Summarize the findings from audio processing."""
        print("Summarizing findings...")
        summary = self.analyze_speech("Please provide a summary of market research and recent bug reports.")
        return summary

# Initialize SageAuditory
sage_auditory = SageAuditory()
sage_auditory.initialize()

# Process some hypothetical audio data
audio_data = "I need to review the latest code for potential bugs."
print(sage_auditory.process_audio(audio_data))

# Summarize findings
summary = sage_auditory.summarize_findings()
print(f"Summary: {summary}")