Create main.py

Main.py

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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)