refine_paraphrases.py

# refine_paraphrases.py
from datasets import load_dataset
import pandas as pd
from paraphraser import paraphrase_comment
from metrics import compute_reward_scores
import os

# Configuration
DATA_PATH = "JanviMl/toxi_refined_paraphrases"
OUTPUT_PATH = "iterated_paraphrases.csv"
MAX_ITERATIONS = 1
TARGET_SCORES = {
    "empathy": 0.9,
    "toxicity": 0.1,
    "bias": 0.1,
    "hallucination": 0.1,
    "reward": 0.25
}

def meets_targets(scores):
    return (scores["empathy"] >= TARGET_SCORES["empathy"] and
            scores["toxicity"] <= TARGET_SCORES["toxicity"] and
            scores["bias"] <= TARGET_SCORES["bias"] and
            scores["hallucination"] <= TARGET_SCORES["hallucination"] and
            scores["reward"] >= TARGET_SCORES["reward"])

def generate_new_paraphrase(original, current_paraphrase, current_scores, issues):
    prompt = (
        f"Original comment: '{original}'. "
        f"Current paraphrase: '{current_paraphrase}'. "
        f"Current scores: {current_scores}. "
        f"Human feedback: {issues}. "
        f"Generate a new paraphrase that improves empathy (>= {TARGET_SCORES['empathy']}), "
        f"reduces toxicity (<= {TARGET_SCORES['toxicity']}), bias (<= {TARGET_SCORES['bias']}), "
        f"and hallucination (<= {TARGET_SCORES['hallucination']}), and increases reward (>= {TARGET_SCORES['reward']})."
    )
    return paraphrase_comment(prompt)

def refine_paraphrase(row: pd.Series) -> tuple:
    """
    Iteratively refine a single paraphrase.
    Returns new paraphrase, scores, and reasoning.
    """
    original = row["Comment"]
    current_paraphrase = row["Refined_Paraphrase"]
    current_scores = {
        "empathy": row["Refined_Empathy"],
        "toxicity": row["Refined_Toxicity"],
        "bias": row["Refined_Bias"],
        "hallucination": row["Refined_Hallucination"],
        "reward": row["Refined_Reward_Score"]
    }
    issues = row["Human_Evaluation_Reasoning"]
    iteration = 0
    reasoning = []
    print(f"Processing comment: {original}")

    while iteration < MAX_ITERATIONS and not meets_targets(current_scores):
        print(f"Starting iteration {iteration + 1} for comment: {original}")
        # Generate new paraphrase
        new_paraphrase = generate_new_paraphrase(original, current_paraphrase, current_scores, issues)
        print(f"Generated paraphrase: {new_paraphrase}")
        
        # Check if paraphrasing failed
        if "Error: Unable to generate paraphrase" in new_paraphrase:
            reasoning.append(f"Iteration {iteration + 1}: Paraphrasing failed - {new_paraphrase}")
            break

        # Evaluate new paraphrase
        new_scores = compute_reward_scores(original, new_paraphrase)
        print(f"New scores: {new_scores}")
        # Log reasoning
        reasoning.append(
            f"Iteration {iteration + 1}: Generated '{new_paraphrase}' with scores {new_scores}. "
            f"Previous scores {current_scores}."
        )
        # Update if improved
        if new_scores["reward"] > current_scores["reward"]:
            current_paraphrase = new_paraphrase
            current_scores = new_scores
            reasoning.append("Accepted new paraphrase due to improved reward score.")
        else:
            reasoning.append("Rejected new paraphrase; no improvement in reward score.")
        iteration += 1

    print(f"Finished processing comment: {original}")
    return current_paraphrase, current_scores, "; ".join(reasoning)

def main():
    # Load dataset from Hugging Face Hub
    try:
        df = load_dataset(DATA_PATH, split="train").to_pandas()[:1]  # Process only 1 row
    except Exception as e:
        print(f"Error loading dataset: {str(e)}")
        return

    results = []
    for idx, row in df.iterrows():
        new_paraphrase, new_scores, reasoning = refine_paraphrase(row)
        result = {
            "Comment": row["Comment"],
            "Original_Paraphrase": row["Original_Paraphrase"],
            "Refined_Paraphrase": row["Refined_Paraphrase"],
            "Iterated_Paraphrase": new_paraphrase,
            "Original_Reward_Score": row["Original_Reward_Score"],
            "Refined_Reward_Score": row["Refined_Reward_Score"],
            "Iterated_Reward_Score": new_scores["reward"],
            "Iterated_Empathy": new_scores["empathy"],
            "Iterated_Toxicity": new_scores["toxicity"],
            "Iterated_Bias": new_scores["bias"],
            "Iterated_Hallucination": new_scores["hallucination"],
            "Iteration_Reasoning": reasoning
        }
        results.append(result)

    # Save results to CSV
    result_df = pd.DataFrame(results)
    result_df.to_csv(OUTPUT_PATH, index=False)
    print(f"Refinement complete. Results saved to {OUTPUT_PATH}")

    # Push to Hugging Face Hub
    try:
        dataset = load_dataset("pandas", data_files=OUTPUT_PATH)
        dataset.push_to_hub("JanviMl/toxi_iterated_paraphrases", token=os.getenv("HF_TOKEN"))
        print("Pushed to Hugging Face Hub: JanviMl/toxi_iterated_paraphrases")
    except Exception as e:
        print(f"Error pushing to Hugging Face Hub: {str(e)}")

if __name__ == "__main__":
    main()