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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) |