farouk1/tweet-optimizer-v2
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Farouk Adeleke
2025-10-20 17:30:18 -04:00
parent 4bc660da2a
commit 92cd054fa5
4 changed files with 13 additions and 10 deletions
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2
README.md
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@@ -1,4 +1,4 @@
# DSPy Tweet Optimizer # DSPy Tweet Optimizer - Modaic Agent
![Twitter X Logo](https://img.freepik.com/free-vector/twitter-new-2023-x-logo-white-background-vector_1017-45422.jpg?semt=ais_hybrid&w=740&q=80) ![Twitter X Logo](https://img.freepik.com/free-vector/twitter-new-2023-x-logo-white-background-vector_1017-45422.jpg?semt=ais_hybrid&w=740&q=80)

3
agent/__init__.py
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@@ -0,0 +1,3 @@
from .index import TweetOptimizerAgent, TweetOptimizerConfig
__all__ = ["TweetOptimizerAgent", "TweetOptimizerConfig"]

16
agent/hill_climbing.py
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@@ -32,7 +32,7 @@ class HillClimbingOptimizer:
Yields: Yields:
Tuple of (current_tweet, evaluation_result, is_improvement, patience_counter, generator_inputs, evaluator_inputs) Tuple of (current_tweet, evaluation_result, is_improvement, patience_counter, generator_inputs, evaluator_inputs)
""" """
# Generate initial tweet # generate initial tweet
generator_inputs = { generator_inputs = {
"input_text": initial_text, "input_text": initial_text,
"current_tweet": "", "current_tweet": "",
@@ -67,10 +67,10 @@ class HillClimbingOptimizer:
evaluator_inputs, evaluator_inputs,
) )
for iteration in range(1, self.max_iterations): for _ in range(1, self.max_iterations):
# Generate improved tweet with previous evaluation as feedback # generate improved tweet with previous evaluation as feedback
try: try:
# Format evaluation for display in generator inputs # format evaluation for display in generator inputs
eval_text = format_evaluation_for_generator(best_score) eval_text = format_evaluation_for_generator(best_score)
generator_inputs = { generator_inputs = {
@@ -85,7 +85,7 @@ class HillClimbingOptimizer:
previous_evaluation=best_score, previous_evaluation=best_score,
) )
# Evaluate candidate # evaluate candidate
evaluator_inputs = { evaluator_inputs = {
"original_text": initial_text, "original_text": initial_text,
"current_best_tweet": best_tweet, "current_best_tweet": best_tweet,
@@ -98,7 +98,7 @@ class HillClimbingOptimizer:
current_best_tweet=best_tweet, current_best_tweet=best_tweet,
) )
# Check if candidate is better (hill climbing condition) # check if candidate is better (hill climbing condition)
is_improvement = candidate_score > best_score is_improvement = candidate_score > best_score
if is_improvement: if is_improvement:
@@ -124,12 +124,12 @@ class HillClimbingOptimizer:
evaluator_inputs, evaluator_inputs,
) )
# Early stopping if no improvement for 'patience' iterations # early stopping if no improvement for 'patience' iterations
if patience_counter >= self.patience: if patience_counter >= self.patience:
break break
except Exception as e: except Exception as e:
# If generation fails, yield current best # if generation fails, yield current best
patience_counter += 1 patience_counter += 1
evaluator_inputs = { evaluator_inputs = {
"original_text": initial_text, "original_text": initial_text,

2
main.py
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@@ -1,4 +1,4 @@
from agent.index import TweetOptimizerAgent, TweetOptimizerConfig from agent import TweetOptimizerAgent, TweetOptimizerConfig
def main(): def main():