diff --git a/contentatscale_detect.py b/contentatscale_detect.py
index 8ac7ca7..d095682 100644
--- a/contentatscale_detect.py
+++ b/contentatscale_detect.py
@@ -17,7 +17,7 @@ def make_req(text : str) -> Optional[str]:
 		'Referer': 'https://contentatscale.ai/ai-content-detector/' 
     }
     data = 'content=' + urllib.parse.quote_plus(text) + '&action=checkaiscore'
-    c = httpx.Client(http2=True, timeout=20.0)
+    c = httpx.Client(http2=True, timeout=30.0)
     res = c.post(API_URL, headers=headers, data=data)
     if res.status_code != 200:
         print(res.text)
diff --git a/test_contentatscale_detect.py b/test_contentatscale_detect.py
index b4c2c80..381ab85 100644
--- a/test_contentatscale_detect.py
+++ b/test_contentatscale_detect.py
@@ -8,183 +8,183 @@ AI_SAMPLE_DIR = 'samples/llm-generated/'
 HUMAN_SAMPLE_DIR = 'samples/human-generated/'
 
 MIN_LEN = 150
-NUM_JSONL_SAMPLES = 500
+NUM_JSONL_SAMPLES = 15#500
 
 ai_files = os.listdir(AI_SAMPLE_DIR)
 human_files = os.listdir(HUMAN_SAMPLE_DIR)
 
 CONFIDENCE_THRESHOLD : float = 0.00 # What confidence to treat as error vs warning
 
-# def test_training_file(record_property):
-#     res = run_on_file_chunked('ai-generated.txt')
-#     if res is None:
-#         pytest.skip('Unable to classify')
-#     (classification, score) = res
-#     record_property("score", str(score))
-#     assert classification == 'AI', 'The training corpus should always be detected as AI-generated... since it is (score: ' + str(round(score, 8)) + ')'
+def test_training_file(record_property):
+    res = run_on_file_chunked('ai-generated.txt')
+    if res is None:
+        pytest.skip('Unable to classify')
+    (classification, score) = res
+    record_property("score", str(score))
+    assert classification == 'AI', 'The training corpus should always be detected as AI-generated... since it is (score: ' + str(round(score, 8)) + ')'
 
-# @pytest.mark.parametrize('f', human_files)
-# def test_human_samples(f, record_property):
-#     res = run_on_file_chunked(HUMAN_SAMPLE_DIR + f)
-#     if res is None:
-#         pytest.skip('Unable to classify')
-#     (classification, score) = res
-#     record_property("score", str(score))
-#     if score > CONFIDENCE_THRESHOLD:
-#         assert classification == 'Human', f + ' is a human-generated file, misclassified as AI-generated with confidence ' + str(round(score, 8))
-#     else:
-#         if classification != 'Human':
-#             warn("Misclassified " + f + " with score of: " + str(round(score, 8)))
-#         else:
-#             warn("Unable to confidently classify: " + f)
+@pytest.mark.parametrize('f', human_files)
+def test_human_samples(f, record_property):
+    res = run_on_file_chunked(HUMAN_SAMPLE_DIR + f)
+    if res is None:
+        pytest.skip('Unable to classify')
+    (classification, score) = res
+    record_property("score", str(score))
+    if score > CONFIDENCE_THRESHOLD:
+        assert classification == 'Human', f + ' is a human-generated file, misclassified as AI-generated with confidence ' + str(round(score, 8))
+    else:
+        if classification != 'Human':
+            warn("Misclassified " + f + " with score of: " + str(round(score, 8)))
+        else:
+            warn("Unable to confidently classify: " + f)
 
-# @pytest.mark.parametrize('f', ai_files)
-# def test_llm_sample(f, record_property):
-#    res = run_on_file_chunked(AI_SAMPLE_DIR + f)
-#    if res is None:
-#         pytest.skip('Unable to classify')
-#    (classification, score) = res
-#    record_property("score", str(score))
-#    if score > CONFIDENCE_THRESHOLD:
-#        assert classification == 'AI', f + ' is an LLM-generated file, misclassified as human-generated with confidence ' + str(round(score, 8))
-#    else:
-#        if classification != 'AI':
-#            warn("Misclassified " + f + " with score of: " + str(round(score, 8)))
-#        else:
-#            warn("Unable to confidently classify: " + f)
+@pytest.mark.parametrize('f', ai_files)
+def test_llm_sample(f, record_property):
+   res = run_on_file_chunked(AI_SAMPLE_DIR + f)
+   if res is None:
+        pytest.skip('Unable to classify')
+   (classification, score) = res
+   record_property("score", str(score))
+   if score > CONFIDENCE_THRESHOLD:
+       assert classification == 'AI', f + ' is an LLM-generated file, misclassified as human-generated with confidence ' + str(round(score, 8))
+   else:
+       if classification != 'AI':
+           warn("Misclassified " + f + " with score of: " + str(round(score, 8)))
+       else:
+           warn("Unable to confidently classify: " + f)
 
-# HUMAN_JSONL_FILE = 'samples/webtext.test.jsonl'
-# human_samples = []
-# with jsonlines.open(HUMAN_JSONL_FILE) as reader:
-#     for obj in reader:
-#         human_samples.append(obj)
+HUMAN_JSONL_FILE = 'samples/webtext.test.jsonl'
+human_samples = []
+with jsonlines.open(HUMAN_JSONL_FILE) as reader:
+    for obj in reader:
+        human_samples.append(obj)
 
-# @pytest.mark.parametrize('i', human_samples[0:NUM_JSONL_SAMPLES])
-# def test_human_jsonl(i, record_property):
-#     res = run_on_text_chunked(i.get('text', ''))
-#     if res is None:
-#         pytest.skip('Unable to classify')
-#     (classification, score) = res
-#     record_property("score", str(score))
-#     assert classification == 'Human', HUMAN_JSONL_FILE + ':' + str(i.get('id')) + ' (len: ' + str(i.get('length', -1)) + ') is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
+@pytest.mark.parametrize('i', human_samples[0:NUM_JSONL_SAMPLES])
+def test_human_jsonl(i, record_property):
+    res = run_on_text_chunked(i.get('text', ''))
+    if res is None:
+        pytest.skip('Unable to classify')
+    (classification, score) = res
+    record_property("score", str(score))
+    assert classification == 'Human', HUMAN_JSONL_FILE + ':' + str(i.get('id')) + ' (len: ' + str(i.get('length', -1)) + ') is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
 
-# AI_JSONL_FILE = 'samples/xl-1542M.test.jsonl'
-# ai_samples = []
-# with jsonlines.open(AI_JSONL_FILE) as reader:
-#     for obj in reader:
-#         ai_samples.append(obj)
+AI_JSONL_FILE = 'samples/xl-1542M.test.jsonl'
+ai_samples = []
+with jsonlines.open(AI_JSONL_FILE) as reader:
+    for obj in reader:
+        ai_samples.append(obj)
 
-# @pytest.mark.parametrize('i', ai_samples[0:NUM_JSONL_SAMPLES])
-# def test_llm_jsonl(i, record_property):
-#     res = run_on_text_chunked(i.get('text', ''))
-#     if res is None:
-#         pytest.skip('Unable to classify')
-#     (classification, score) = res
-#     record_property("score", str(score))
-#     assert classification == 'AI', AI_JSONL_FILE + ':' + str(i.get('id')) + ' (text: ' + i.get('text', "").replace('\n', ' ')[:50] + ') is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
+@pytest.mark.parametrize('i', ai_samples[0:NUM_JSONL_SAMPLES])
+def test_llm_jsonl(i, record_property):
+    res = run_on_text_chunked(i.get('text', ''))
+    if res is None:
+        pytest.skip('Unable to classify')
+    (classification, score) = res
+    record_property("score", str(score))
+    assert classification == 'AI', AI_JSONL_FILE + ':' + str(i.get('id')) + ' (text: ' + i.get('text', "").replace('\n', ' ')[:50] + ') is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
 
-# GPT3_JSONL_FILE = 'samples/GPT-3-175b_samples.jsonl'
-# gpt3_samples = []
-# with jsonlines.open(GPT3_JSONL_FILE) as reader:
-#     for o in reader:
-#         for l in o.split('<|endoftext|>'):
-#             if len(l) >= MIN_LEN:
-#                 gpt3_samples.append(l)
+GPT3_JSONL_FILE = 'samples/GPT-3-175b_samples.jsonl'
+gpt3_samples = []
+with jsonlines.open(GPT3_JSONL_FILE) as reader:
+    for o in reader:
+        for l in o.split('<|endoftext|>'):
+            if len(l) >= MIN_LEN:
+                gpt3_samples.append(l)
 
-# @pytest.mark.parametrize('i', gpt3_samples[0:NUM_JSONL_SAMPLES])
-# def test_gpt3_jsonl(i, record_property):
-#     res = run_on_text_chunked(i)
-#     if res is None:
-#         pytest.skip('Unable to classify')
-#     (classification, score) = res
-#     record_property("score", str(score))
-#     assert classification == 'AI', GPT3_JSONL_FILE + ' is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
+@pytest.mark.parametrize('i', gpt3_samples[0:NUM_JSONL_SAMPLES])
+def test_gpt3_jsonl(i, record_property):
+    res = run_on_text_chunked(i)
+    if res is None:
+        pytest.skip('Unable to classify')
+    (classification, score) = res
+    record_property("score", str(score))
+    assert classification == 'AI', GPT3_JSONL_FILE + ' is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
 
-# NEWS_JSONL_FILE = 'samples/news.jsonl'
-# news_samples = []
-# with jsonlines.open(NEWS_JSONL_FILE) as reader:
-#     for obj in reader:
-#         news_samples.append(obj)
+NEWS_JSONL_FILE = 'samples/news.jsonl'
+news_samples = []
+with jsonlines.open(NEWS_JSONL_FILE) as reader:
+    for obj in reader:
+        news_samples.append(obj)
 
-# @pytest.mark.parametrize('i', news_samples[0:NUM_JSONL_SAMPLES])
-# def test_humannews_jsonl(i, record_property):
-#     res = run_on_text_chunked(i.get('human', ''))
-#     if res is None:
-#         pytest.skip('Unable to classify')
-#     (classification, score) = res
-#     record_property("score", str(score))
-#     assert classification == 'Human', NEWS_JSONL_FILE + ' is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
+@pytest.mark.parametrize('i', news_samples[0:NUM_JSONL_SAMPLES])
+def test_humannews_jsonl(i, record_property):
+    res = run_on_text_chunked(i.get('human', ''))
+    if res is None:
+        pytest.skip('Unable to classify')
+    (classification, score) = res
+    record_property("score", str(score))
+    assert classification == 'Human', NEWS_JSONL_FILE + ' is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
 
-# @pytest.mark.parametrize('i', news_samples[0:NUM_JSONL_SAMPLES])
-# def test_chatgptnews_jsonl(i, record_property):
-#     res = run_on_text_chunked(i.get('chatgpt', ''))
-#     if res is None:
-#         pytest.skip('Unable to classify')
-#     (classification, score) = res
-#     record_property("score", str(score))
-#     assert classification == 'AI', NEWS_JSONL_FILE + ' is a AI-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
+@pytest.mark.parametrize('i', news_samples[0:NUM_JSONL_SAMPLES])
+def test_chatgptnews_jsonl(i, record_property):
+    res = run_on_text_chunked(i.get('chatgpt', ''))
+    if res is None:
+        pytest.skip('Unable to classify')
+    (classification, score) = res
+    record_property("score", str(score))
+    assert classification == 'AI', NEWS_JSONL_FILE + ' is a AI-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
 
-# CHEAT_HUMAN_JSONL_FILE = 'samples/ieee-init.jsonl'
-# ch_samples = []
-# with jsonlines.open(CHEAT_HUMAN_JSONL_FILE) as reader:
-#     for obj in reader:
-#         if len(obj.get('abstract', '')) >= MIN_LEN:
-#             ch_samples.append(obj)
+CHEAT_HUMAN_JSONL_FILE = 'samples/ieee-init.jsonl'
+ch_samples = []
+with jsonlines.open(CHEAT_HUMAN_JSONL_FILE) as reader:
+    for obj in reader:
+        if len(obj.get('abstract', '')) >= MIN_LEN:
+            ch_samples.append(obj)
 
-# @pytest.mark.parametrize('i', ch_samples[0:NUM_JSONL_SAMPLES])
-# def test_cheat_human_jsonl(i, record_property):
-#     res = run_on_text_chunked(i.get('abstract', ''))
-#     if res is None:
-#         pytest.skip('Unable to classify')
-#     (classification, score) = res
-#     record_property("score", str(score))
-#     assert classification == 'Human', CHEAT_HUMAN_JSONL_FILE + ':' + str(i.get('id')) + ' [' + str(len(i.get('abstract', ''))) + '] (title: ' + i.get('title', "").replace('\n', ' ')[:15] + ') is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
+@pytest.mark.parametrize('i', ch_samples[0:NUM_JSONL_SAMPLES])
+def test_cheat_human_jsonl(i, record_property):
+    res = run_on_text_chunked(i.get('abstract', ''))
+    if res is None:
+        pytest.skip('Unable to classify')
+    (classification, score) = res
+    record_property("score", str(score))
+    assert classification == 'Human', CHEAT_HUMAN_JSONL_FILE + ':' + str(i.get('id')) + ' [' + str(len(i.get('abstract', ''))) + '] (title: ' + i.get('title', "").replace('\n', ' ')[:15] + ') is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
 
-# CHEAT_GEN_JSONL_FILE = 'samples/ieee-chatgpt-generation.jsonl'
-# cg_samples = []
-# with jsonlines.open(CHEAT_GEN_JSONL_FILE) as reader:
-#     for obj in reader:
-#         if len(obj.get('abstract', '')) >= MIN_LEN:
-#             cg_samples.append(obj)
+CHEAT_GEN_JSONL_FILE = 'samples/ieee-chatgpt-generation.jsonl'
+cg_samples = []
+with jsonlines.open(CHEAT_GEN_JSONL_FILE) as reader:
+    for obj in reader:
+        if len(obj.get('abstract', '')) >= MIN_LEN:
+            cg_samples.append(obj)
 
-# @pytest.mark.parametrize('i', cg_samples[0:NUM_JSONL_SAMPLES])
-# def test_cheat_generation_jsonl(i, record_property):
-#     res = run_on_text_chunked(i.get('abstract', ''))
-#     if res is None:
-#         pytest.skip('Unable to classify')
-#     (classification, score) = res
-#     record_property("score", str(score))
-#     assert classification == 'AI', CHEAT_GEN_JSONL_FILE + ':' + str(i.get('id')) + ' (title: ' + i.get('title', "").replace('\n', ' ')[:50] + ') is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
+@pytest.mark.parametrize('i', cg_samples[0:NUM_JSONL_SAMPLES])
+def test_cheat_generation_jsonl(i, record_property):
+    res = run_on_text_chunked(i.get('abstract', ''))
+    if res is None:
+        pytest.skip('Unable to classify')
+    (classification, score) = res
+    record_property("score", str(score))
+    assert classification == 'AI', CHEAT_GEN_JSONL_FILE + ':' + str(i.get('id')) + ' (title: ' + i.get('title', "").replace('\n', ' ')[:50] + ') is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
 
-# CHEAT_POLISH_JSONL_FILE = 'samples/ieee-chatgpt-polish.jsonl'
-# cp_samples = []
-# with jsonlines.open(CHEAT_POLISH_JSONL_FILE) as reader:
-#     for obj in reader:
-#         if len(obj.get('abstract', '')) >= MIN_LEN:
-#             cp_samples.append(obj)
+CHEAT_POLISH_JSONL_FILE = 'samples/ieee-chatgpt-polish.jsonl'
+cp_samples = []
+with jsonlines.open(CHEAT_POLISH_JSONL_FILE) as reader:
+    for obj in reader:
+        if len(obj.get('abstract', '')) >= MIN_LEN:
+            cp_samples.append(obj)
 
-# @pytest.mark.parametrize('i', cp_samples[0:NUM_JSONL_SAMPLES])
-# def test_cheat_polish_jsonl(i, record_property):
-#     res = run_on_text_chunked(i.get('abstract', ''))
-#     if res is None:
-#         pytest.skip('Unable to classify')
-#     (classification, score) = res
-#     record_property("score", str(score))
-#     assert classification == 'AI', CHEAT_POLISH_JSONL_FILE + ':' + str(i.get('id')) + ' (title: ' + i.get('title', "").replace('\n', ' ')[:50] + ') is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
+@pytest.mark.parametrize('i', cp_samples[0:NUM_JSONL_SAMPLES])
+def test_cheat_polish_jsonl(i, record_property):
+    res = run_on_text_chunked(i.get('abstract', ''))
+    if res is None:
+        pytest.skip('Unable to classify')
+    (classification, score) = res
+    record_property("score", str(score))
+    assert classification == 'AI', CHEAT_POLISH_JSONL_FILE + ':' + str(i.get('id')) + ' (title: ' + i.get('title', "").replace('\n', ' ')[:50] + ') is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
 
 
-# CHEAT_VICUNAGEN_JSONL_FILE = 'samples/ieee-vicuna-generation.jsonl'
-# vg_samples = []
-# with jsonlines.open(CHEAT_VICUNAGEN_JSONL_FILE) as reader:
-#     for obj in reader:
-#         if len(obj.get('abstract', '')) >= MIN_LEN:
-#             vg_samples.append(obj)
+CHEAT_VICUNAGEN_JSONL_FILE = 'samples/ieee-vicuna-generation.jsonl'
+vg_samples = []
+with jsonlines.open(CHEAT_VICUNAGEN_JSONL_FILE) as reader:
+    for obj in reader:
+        if len(obj.get('abstract', '')) >= MIN_LEN:
+            vg_samples.append(obj)
 
-# @pytest.mark.parametrize('i', vg_samples[0:NUM_JSONL_SAMPLES])
-# def test_vicuna_generation_jsonl(i, record_property):
-#     (classification, score) = run_on_text_chunked(i.get('abstract', ''), fuzziness=FUZZINESS, prelude_ratio=PRELUDE_RATIO)
-#     record_property("score", str(score))
-#     assert classification == 'AI', CHEAT_VICUNAGEN_JSONL_FILE + ':' + str(i.get('id')) + ' (title: ' + i.get('title', "").replace('\n', ' ')[:50] + ') is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
+@pytest.mark.parametrize('i', vg_samples[0:NUM_JSONL_SAMPLES])
+def test_vicuna_generation_jsonl(i, record_property):
+    (classification, score) = run_on_text_chunked(i.get('abstract', ''))
+    record_property("score", str(score))
+    assert classification == 'AI', CHEAT_VICUNAGEN_JSONL_FILE + ':' + str(i.get('id')) + ' (title: ' + i.get('title', "").replace('\n', ' ')[:50] + ') is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
 
 GPTZERO_EVAL_FILE = 'samples/gptzero_eval.csv'
 ge_samples = []
@@ -194,13 +194,13 @@ with open(GPTZERO_EVAL_FILE) as fp:
         if len(obj.get('Document', '')) >= MIN_LEN:
             ge_samples.append(obj)
 
-@pytest.mark.parametrize('i', list(filter(lambda x: x.get('Label') == 'AI', ge_samples[0:NUM_JSONL_SAMPLES])))
+@pytest.mark.parametrize('i', list(filter(lambda x: x.get('Label') == 'AI', ge_samples))[0:NUM_JSONL_SAMPLES])
 def test_gptzero_eval_dataset_ai(i, record_property):
     (classification, score) = run_on_text_chunked(i.get('Document', ''))
     record_property("score", str(score))
     assert classification == i.get('Label'), GPTZERO_EVAL_FILE + ':' + str(i.get('Index')) + ' was misclassified with confidence ' + str(round(score, 8))
 
-@pytest.mark.parametrize('i', list(filter(lambda x: x.get('Label') == 'Human', ge_samples[0:NUM_JSONL_SAMPLES])))
+@pytest.mark.parametrize('i', list(filter(lambda x: x.get('Label') == 'Human', ge_samples))[0:NUM_JSONL_SAMPLES])
 def test_gptzero_eval_dataset_human(i, record_property):
     (classification, score) = run_on_text_chunked(i.get('Document', ''))
     record_property("score", str(score))