A Coding Information for Constructing a Self-Enhancing AI Agent Utilizing Google’s Gemini API with Clever Adaptation Options

import google.generativeai as genai
import json
import time
import re
from typing import Dict, Listing, Any
from datetime import datetime
import traceback

class SelfImprovingAgent:
    def __init__(self, api_key: str):
        """Initialize the self-improving agent with Gemini API"""
        genai.configure(api_key=api_key)
        self.mannequin = genai.GenerativeModel('gemini-1.5-flash')
       
        self.reminiscence = {
            'successful_strategies': [],
            'failed_attempts': [],
            'learned_patterns': [],
            'performance_metrics': [],
            'code_improvements': []
        }
       
        self.capabilities = {
            'problem_solving': 0.5,
            'code_generation': 0.5,
            'learning_efficiency': 0.5,
            'error_handling': 0.5
        }
       
        self.iteration_count = 0
        self.improvement_history = []
   
    def analyze_task(self, activity: str) -> Dict[str, Any]:
        """Analyze a given activity and decide method"""
        analysis_prompt = f"""
        Analyze this activity and supply a structured method:
        Activity: {activity}
       
        Please present:
        1. Activity complexity (1-10)
        2. Required expertise
        3. Potential challenges
        4. Advisable method
        5. Success standards
       
        Format as JSON.
        """
       
        strive:
            response = self.mannequin.generate_content(analysis_prompt)
            json_match = re.search(r'{.*}', response.textual content, re.DOTALL)
            if json_match:
                return json.hundreds(json_match.group())
            else:
                return {
                    "complexity": 5,
                    "expertise": ["general problem solving"],
                    "challenges": ["undefined requirements"],
                    "method": "iterative enchancment",
                    "success_criteria": ["task completion"]
                }
        besides Exception as e:
            print(f"Activity evaluation error: {e}")
            return {"complexity": 5, "expertise": [], "challenges": [], "method": "fundamental", "success_criteria": []}
   
    def solve_problem(self, downside: str) -> Dict[str, Any]:
        """Try to resolve an issue utilizing present capabilities"""
        self.iteration_count += 1
        print(f"n=== Iteration {self.iteration_count} ===")
        print(f"Downside: {downside}")
       
        task_analysis = self.analyze_task(downside)
        print(f"Activity Evaluation: {task_analysis}")
       
        solution_prompt = f"""
        Based mostly on my earlier studying and capabilities, remedy this downside:
        Downside: {downside}
       
        My present capabilities: {self.capabilities}
        Earlier profitable methods: {self.reminiscence['successful_strategies'][-3:]}  # Final 3
        Identified patterns: {self.reminiscence['learned_patterns'][-3:]}  # Final 3
       
        Present an in depth resolution with:
        1. Step-by-step method
        2. Code implementation (if relevant)
        3. Anticipated consequence
        4. Potential enhancements
        """
       
        strive:
            start_time = time.time()
            response = self.mannequin.generate_content(solution_prompt)
            solve_time = time.time() - start_time
           
            resolution = {
                'downside': downside,
                'resolution': response.textual content,
                'solve_time': solve_time,
                'iteration': self.iteration_count,
                'task_analysis': task_analysis
            }
           
            quality_score = self.evaluate_solution(resolution)
            resolution['quality_score'] = quality_score
           
            self.reminiscence['performance_metrics'].append({
                'iteration': self.iteration_count,
                'high quality': quality_score,
                'time': solve_time,
                'complexity': task_analysis.get('complexity', 5)
            })
           
            if quality_score > 0.7:
                self.reminiscence['successful_strategies'].append(resolution)
                print(f"✅ Resolution High quality: {quality_score:.2f} (Success)")
            else:
                self.reminiscence['failed_attempts'].append(resolution)
                print(f"❌ Resolution High quality: {quality_score:.2f} (Wants Enchancment)")
           
            return resolution
           
        besides Exception as e:
            print(f"Downside fixing error: {e}")
            error_solution = {
                'downside': downside,
                'resolution': f"Error occurred: {str(e)}",
                'solve_time': 0,
                'iteration': self.iteration_count,
                'quality_score': 0.0,
                'error': str(e)
            }
            self.reminiscence['failed_attempts'].append(error_solution)
            return error_solution
   
    def evaluate_solution(self, resolution: Dict[str, Any]) -> float:
        """Consider the standard of an answer"""
        evaluation_prompt = f"""
        Consider this resolution on a scale of 0.0 to 1.0:
       
        Downside: {resolution['problem']}
        Resolution: {resolution['solution'][:500]}...  # Truncated for analysis
       
        Price primarily based on:
        1. Completeness (addresses all features)
        2. Correctness (logically sound)
        3. Readability (nicely defined)
        4. Practicality (implementable)
        5. Innovation (artistic method)
       
        Reply with only a decimal quantity between 0.0 and 1.0.
        """
       
        strive:
            response = self.mannequin.generate_content(evaluation_prompt)
            score_match = re.search(r'(d+.?d*)', response.textual content)
            if score_match:
                rating = float(score_match.group(1))
                return min(max(rating, 0.0), 1.0)  
            return 0.5  
        besides:
            return 0.5
   
    def learn_from_experience(self):
        """Analyze previous efficiency and enhance capabilities"""
        print("n🧠 Studying from expertise...")
       
        if len(self.reminiscence['performance_metrics'])  str:
        """Generate improved model of code"""
        improvement_prompt = f"""
        Enhance this code primarily based on the objective:
       
        Present Code:
        {current_code}
       
        Enchancment Purpose: {improvement_goal}
        My present capabilities: {self.capabilities}
        Realized patterns: {self.reminiscence['learned_patterns'][-3:]}
       
        Present improved code with:
        1. Enhanced performance
        2. Higher error dealing with
        3. Improved effectivity
        4. Clear feedback explaining enhancements
        """
       
        strive:
            response = self.mannequin.generate_content(improvement_prompt)
           
            improved_code = {
                'authentic': current_code,
                'improved': response.textual content,
                'objective': improvement_goal,
                'iteration': self.iteration_count
            }
           
            self.reminiscence['code_improvements'].append(improved_code)
            return response.textual content
           
        besides Exception as e:
            print(f"Code enchancment error: {e}")
            return current_code
   
    def self_modify(self):
        """Try to enhance the agent's personal code"""
        print("n🔧 Trying self-modification...")
       
        current_method = """
        def solve_problem(self, downside: str) -> Dict[str, Any]:
            # Present implementation
            cross
        """
       
        improved_method = self.generate_improved_code(
            current_method,
            "Make downside fixing extra environment friendly and correct"
        )
       
        print("Generated improved technique construction")
        print("Word: Precise self-modification requires cautious implementation in manufacturing")
   
    def run_improvement_cycle(self, issues: Listing[str], cycles: int = 3):
        """Run a whole enchancment cycle"""
        print(f"🚀 Beginning {cycles} enchancment cycles with {len(issues)} issues")
       
        for cycle in vary(cycles):
            print(f"n{'='*50}")
            print(f"IMPROVEMENT CYCLE {cycle + 1}/{cycles}")
            print(f"{'='*50}")
           
            cycle_results = []
            for downside in issues:
                outcome = self.solve_problem(downside)
                cycle_results.append(outcome)
                time.sleep(1)  
           
            self.learn_from_experience()
           
            if cycle  str:
        """Generate a complete efficiency report"""
        if not self.reminiscence['performance_metrics']:
            return "No efficiency knowledge accessible but."
       
        metrics = self.reminiscence['performance_metrics']
        avg_quality = sum(m['quality'] for m in metrics) / len(metrics)
        avg_time = sum(m['time'] for m in metrics) / len(metrics)
       
        report = f"""
        📈 AGENT PERFORMANCE REPORT
        {'='*40}
       
        Complete Iterations: {self.iteration_count}
        Common Resolution High quality: {avg_quality:.3f}
        Common Remedy Time: {avg_time:.2f}s
       
        Profitable Options: {len(self.reminiscence['successful_strategies'])}
        Failed Makes an attempt: {len(self.reminiscence['failed_attempts'])}
        Success Price: {len(self.reminiscence['successful_strategies']) / max(1, self.iteration_count) * 100:.1f}%
       
        Present Capabilities:
        {json.dumps(self.capabilities, indent=2)}
       
        Patterns Realized: {len(self.reminiscence['learned_patterns'])}
        Code Enhancements: {len(self.reminiscence['code_improvements'])}
        """
       
        return report

def principal():
    """Foremost perform to exhibit the self-improving agent"""
   
    API_KEY = "Use Your GEMINI KEY Right here"
   
    if API_KEY == "Use Your GEMINI KEY Right here":
        print("⚠️  Please set your Gemini API key within the API_KEY variable")
        print("Get your API key from: https://makersuite.google.com/app/apikey")
        return
   
    agent = SelfImprovingAgent(API_KEY)
   
    test_problems = [
        "Write a function to calculate the factorial of a number",
        "Create a simple text-based calculator that handles basic operations",
        "Design a system to find the shortest path between two points in a graph",
        "Implement a basic recommendation system for movies based on user preferences",
        "Create a machine learning model to predict house prices based on features"
    ]
   
    print("🤖 Self-Enhancing Agent Demo")
    print("This agent will try to resolve issues and enhance over time")
   
    agent.run_improvement_cycle(test_problems, cycles=3)
   
    print("n" + agent.get_performance_report())
   
    print("n" + "="*50)
    print("TESTING IMPROVED AGENT")
    print("="*50)
   
    final_problem = "Create an environment friendly algorithm to kind a big dataset"
    final_result = agent.solve_problem(final_problem)
   
    print(f"nFinal Downside Resolution High quality: {final_result.get('quality_score', 0):.2f}")

def setup_instructions():
    """Print setup directions for Google Colab"""
    directions = """
    📋 SETUP INSTRUCTIONS FOR GOOGLE COLAB:
   
    1. Set up the Gemini API consumer:
       !pip set up google-generativeai
   
    2. Get your Gemini API key:
       - Go to https://makersuite.google.com/app/apikey
       - Create a brand new API key
       - Copy the important thing
   
    3. Substitute 'your-gemini-api-key-here' together with your precise API key
   
    4. Run the code!
   
    🔧 CUSTOMIZATION OPTIONS:
    - Modify test_problems checklist so as to add your individual challenges
    - Alter enchancment cycles rely
    - Add new capabilities to trace
    - Prolong the educational mechanisms
   
    💡 IMPROVEMENT IDEAS:
    - Add persistent reminiscence (save/load agent state)
    - Implement extra refined analysis metrics
    - Add domain-specific downside varieties
    - Create visualization of enchancment over time
    """
    print(directions)


if __name__ == "__main__":
    setup_instructions()
    print("n" + "="*60)
    principal()