import os

# API Key Integration (Ensure these are set in Colab secrets)
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY")
os.environ["HUGGINGFACE_HUB_TOKEN"] = os.environ.get("HUGGINGFACE_API_KEY")

# ... (Rest of the code)import ast
import json
import networkx as nx
import os
import sqlite3

from transformers import pipeline

# API Key Integration (Ensure these are set in Colab secrets)
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY")
os.environ["HUGGINGFACE_HUB_TOKEN"] = os.environ.get("HUGGINGFACE_API_KEY")

class Learner:
    def __init__(self):
        self.knowledge = pipeline('question-answering')
        # Placeholder: Initialize code comprehension model (e.g., using transformers)

    def learn(self, context, question):
        answer = self.knowledge(question=question, context=context)
        return answer['answer']

    def comprehend_code(self, code_snippet, language="python"):
        tree = ast.parse(code_snippet)
        for node in ast.walk(tree):
            if isinstance(node, ast.FunctionDef):
                print(f"Function definition: {node.name}")
                # Analyze function arguments, return type, etc.
                for arg in node.args.args:
                    print(f"  Argument: {arg.arg}")
                if node.returns:
                    print(f"  Return type: {node.returns}")
            elif isinstance(node, ast.ClassDef):
                print(f"Class definition: {node.name}")
                # Analyze class attributes, methods, inheritance, etc.
                for base in node.bases:
                    print(f"  Inherits from: {base.id}")
            elif isinstance(node, ast.Assign):
                print(f"Variable assignment: {node.targets[0].id} = {node.value}")
                # Analyze variable type, scope, etc.
            elif isinstance(node, ast.If):
                print(f"Conditional statement: if {node.test}")
                # Analyze condition, branches, etc.
            # Add more analysis for other node types (loops, imports, etc.)

    def generate_code(self, instructions, language="python"):
        generator = pipeline('code-generation', model='Salesforce/codegen-350M-mono')
        generated_code = generator(instructions)
        return generated_code[0]['generated_text']

    def debug_and_optimize(self, code_snippet, language="python"):
        # Use pylint for static analysis
        results = pylint.lint.Run(code_snippet, do_exit=False)
        for msg in results.linter.reporter.messages:
            print(f"{msg.msg_id}: {msg.msg} ({msg.line},{msg.column})")

        # Placeholder: Add dynamic analysis or other optimization techniques

class Observer:
    def __init__(self):
        self.sentiment = pipeline('sentiment-analysis')

    def analyze_sentiment(self, text):
        result = self.sentiment(text)[0]
        return result['label']

class GoalSeeker:
    def __init__(self):
        self.goals = []  # Initialize self.goals to an empty list

    def add_goal(self, goal):
        self.goals.append(goal)

    def pursue_goal(self):
        if self.goals:
            return f"Currently pursuing goal: {self.goals[0]}"
        else:
            return "No goals set yet."

class Communicator:
    def __init__(self):
        self.generator = pipeline('text-generation')

    def express(self, prompt, max_length=50):
        result = self.generator(prompt, max_length=max_length)[0]
        return result['generated_text']

class BrainstormingEngine:
    def __init__(self):
        self.generator = pipeline('text-generation')
        self.idea_repository = nx.Graph()  # Using a graph database

    def store_idea(self, idea, category):
        # Add the idea as a node in the graph
        self.idea_repository.add_node(idea, category=category)
        # Placeholder: Add connections to related ideas based on semantic similarity

    def generate_code_prototype(self, idea):
        # Use Learner to generate code structure based on the idea
        # Placeholder: Implement logic to analyze the idea and generate code
        pass  # Placeholder for implementation

class Synergy:
    def __init__(self, learner, observer, goal_seeker, communicator, brainstorming_engine):
        self.learner = learner
        self.observer = observer
        self.goal_seeker = goal_seeker
        self.communicator = communicator
        self.brainstorming_engine = brainstorming_engine

    def interact(self, user_input):
        sentiment = self.observer.analyze_sentiment(user_input)
        print(f"Sentiment: {sentiment}")

        if "goal" in user_input.lower():
            self.goal_seeker.add_goal(user_input)
            print(self.goal_seeker.pursue_goal())

        # Example of integrating coding and brainstorming
        if "code" in user_input.lower():
            self.integrate_coding_and_brainstorming(user_input)

        response = self.communicator.express(user_input)
        print(f"Synergy: {response}")

    def integrate_coding_and_brainstorming(self, task):
        # 1. Analyze the task using Observer
        sentiment = self.observer.analyze_sentiment(task)
        # ... (Additional analysis of the task)

        # 2. Generate ideas using BrainstormingEngine
        ideas = self.brainstorming_engine.generator(task, num_return_sequences=3)
        for idea in ideas:
            print(f"Idea: {idea['generated_text']}")
            self.brainstorming_engine.store_idea(idea['generated_text'], sentiment)

        # 3. Generate code prototypes using Learner
        for idea in ideas:
            code_prototype = self.learner.generate_code(idea['generated_text'])
            print(f"Code Prototype: {code_prototype}")
            # ... (Further analysis and refinement of the code)

    def learn_from_outcomes(self, feedback):
        # 1. Analyze feedback using Observer
        sentiment = self.observer.analyze_sentiment(feedback)
        # ... (Additional analysis of the feedback)

        # 2. Update internal models and strategies based on feedback
        # ... (Implementation details)

# Create instances of the core cognitive functions
learner = Learner()
observer = Observer()
goal_seeker = GoalSeeker()
communicator = Communicator()
brainstorming_engine = BrainstormingEngine()

# Create an instance of Synergy, passing the necessary arguments
synergy = Synergy(learner, observer, goal_seeker, communicator, brainstorming_engine)