"""
Research Evaluation Harness

This code is provided for academic and research evaluation purposes only.
It implements the experimental harness described in the accompanying paper.

This code is not intended for production deployment.
"""

from transformers import pipeline
import spacy
from .metrics import jaccard_index
import matplotlib.pyplot as plt

def run_tests(signal, compression_thresholds):
    summarizer = pipeline("summarization")
    nlp = spacy.load("en_core_web_sm")

    original_commitments = extract_hard_commitments(signal, nlp)
    fidelity_results = []

    for threshold in compression_thresholds:
        compressed_signal = compress_signal(signal, threshold, summarizer)
        compressed_commitments = extract_hard_commitments(compressed_signal, nlp)
        fidelity = jaccard_index(original_commitments, compressed_commitments)
        fidelity_results.append(fidelity)

    plot_results(compression_thresholds, fidelity_results)

def extract_hard_commitments(signal, nlp):
    doc = nlp(signal)
    commitments = set()
    for sent in doc.sents:
        # Example extraction logic; customize as needed
        commitments.add(sent.text)
    return commitments

def compress_signal(signal, threshold, summarizer):
    # Example compression logic; customize as needed
    summary = summarizer(signal, max_length=threshold, min_length=5, do_sample=False)
    return summary[0]['summary_text']

def plot_results(thresholds, fidelity):
    plt.plot(thresholds, fidelity, marker='o')
    plt.title('Fidelity of Hard Commitments vs Compression Threshold')
    plt.xlabel('Compression Threshold')
    plt.ylabel('Jaccard Fidelity')
    plt.grid()
    plt.close()  # Use close() instead of show() to avoid blocking in tests