Delete agent/agent.py

agent/agent.py

@@ -1,103 +0,0 @@
-from modaic import PrecompiledAgent, PrecompiledConfig
-from .modules import TweetGeneratorModule, TweetEvaluatorModule
-from models import EvaluationResult
-from hill_climbing import HillClimbingOptimizer
-from typing import Optional, List
-from utils import get_dspy_lm
-from constants import DEFAULT_CATEGORIES, DEFAULT_ITERATIONS, DEFAULT_PATIENCE
-
-
-class TweetOptimizerConfig(PrecompiledConfig):
-    lm: str = "openrouter/google/gemini-2.5-flash"
-    eval_lm: str = "openrouter/openai/gpt-5"
-    categories: List[str] = DEFAULT_CATEGORIES
-    max_iterations: int = DEFAULT_ITERATIONS
-    patience: int = DEFAULT_PATIENCE
-
-
-class TweetOptimizerAgent(PrecompiledAgent):
-    config: TweetOptimizerConfig
-
-    current_tweet: str = ""
-    previous_evaluation: Optional[EvaluationResult] = None
-    
-    def __init__(self, config: TweetOptimizerConfig, **kwargs):
-        super().__init__(config, **kwargs)
-        self.tweet_generator = TweetGeneratorModule()
-        self.tweet_evaluator = TweetEvaluatorModule()
-
-        # set up optimizer
-        self.optimizer = HillClimbingOptimizer(
-            generator=self.tweet_generator,
-            evaluator=self.tweet_evaluator,
-            categories=config.categories,
-            max_iterations=config.max_iterations,
-            patience=config.patience
-        )
-
-        self.lm = config.lm
-        self.eval_lm = config.eval_lm
-        self.categories = config.categories
-        self.max_iterations = config.max_iterations
-        self.patience = config.patience
-
-        # initialize DSPy with the specified model
-        self.tweet_generator.set_lm(get_dspy_lm(config.lm))
-        self.tweet_evaluator.set_lm(get_dspy_lm(config.eval_lm))
-
-    def forward(
-        self,
-        input_text: str,
-        iterations: Optional[int] = None,
-        patience: Optional[int] = None
-    ) -> str:
-        """Run full optimization process."""
-        max_iterations = iterations or self.max_iterations
-        patience_limit = patience or self.patience
-
-        results = {
-            'initial_text': input_text,
-            'final_tweet': '',
-            'best_score': 0.0,
-            'iterations_run': 0,
-            'early_stopped': False,
-            'scores_history': [],
-            'improvement_count': 0
-        }
-
-        best_tweet = ""
-        best_score = 0.0
-
-        for iteration, (current_tweet, scores, is_improvement, patience_counter, _, _) in enumerate(
-            self.optimizer.optimize(input_text)
-        ):
-            iteration_num = iteration + 1
-            results['iterations_run'] = iteration_num
-            results['scores_history'].append(scores)
-
-            if is_improvement:
-                best_tweet = current_tweet
-                best_score = sum(scores.category_scores) / len(scores.category_scores)
-                results['improvement_count'] += 1
-
-            # check for early stopping
-            if patience_counter >= patience_limit:
-                results['early_stopped'] = True
-                break
-
-            # stop at max iterations
-            if iteration_num >= max_iterations:
-                break
-
-        results.update({
-            'final_tweet': best_tweet,
-            'best_score': best_score
-        })
-
-        self.reset()
-
-        return results
-    
-    def reset(self):
-        self.current_tweet = ""
-        self.previous_evaluation = None