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from transformers import pipeline
import gradio as gr
import nltk
from nltk.tokenize import sent_tokenize
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import gradio as gr
nltk.download("punkt")
nltk.download('punkt_tab')
model_name = "MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli" #"MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
labels = ["entailment", "neutral", "contradiction"]
def nli(hypothesis, premise):
inputs = tokenizer(premise, hypothesis, return_tensors="pt", truncation=True, max_length=512)
logits = model(**inputs).logits[0]
probs = torch.softmax(logits, -1).tolist()
return dict(zip(labels, probs))
def get_labels(result):
if result["entailment"]> result["neutral"] and result["entailment"]> result["contradiction"]:
return "entailment"
elif result["entailment"]<result["neutral"] and result["contradiction"]<result["neutral"]:
return "neutral"
else:
return "contradiction"
def detect_hallucinations(generated_text, source_text):
"""
Detect intrinsic and extrinsic hallucinations in the generated text.
"""
generated_sentences = sent_tokenize(generated_text)
source_sentences = sent_tokenize(source_text)
intrinsic = []
extrinsic = []
correct_sents = []
for i in range(len(generated_sentences)):
for j in range(len(source_sentences)):
# result = nli([generated_sentences[i], source_sentences[j]])[0]
prediction = nli(generated_sentences[i], source_sentences[j])
label = get_labels(prediction)
score = prediction[label]
result = {"label": label, "score": score}
if result['label'].lower() == "contradiction":
intrinsic.append({
"generated_sentence": generated_sentences[i],
"source_sentence": source_sentences[j],
"contradiction_score": result['score']
})
elif result['label'].lower() == "entailment":
correct_sents.append(generated_sentences[i])
break
if result['label'].lower() == "neutral" and generated_sentences[i] not in correct_sents:
extrinsic.append({
"claim": generated_sentences[i],
"source_sentence": source_sentences[j],
"status": "not_supported",
"confidence": result['score']
})
return {
"intrinsic": intrinsic,
"extrinsic": extrinsic
}
def gradio_interface(generated_text, source_text):
result = detect_hallucinations(generated_text, source_text)
return result
theme = gr.themes.Soft(primary_hue="teal", secondary_hue="blue", neutral_hue="gray").set(
body_text_color="*neutral_900",
block_label_text_color="*neutral_900",
block_title_text_color="*neutral_900"
)
custom_css = """
.gradio-container { background-color: #ffffff !important; }
.gradio-json { font-family: 'Fira Code', monospace; font-size: 14px; color: #1f2937 !important; }
#header_text {
color: #111 !important;
"""
dark_css = """
.gradio-container {
background-color: #000 !important;
color: #eee !important;
}
.gradio-container .gr-block {
background-color: #000 !important;
}
.gradio-container textarea, .gradio-container input {
background-color: #111 !important;
color: #eee !important;
}
.gradio-json {
background-color: #111 !important;
color: #eee !important;
}
#header_text {
color: #eee !important;
}
"""
demo = gr.Blocks(theme=theme, css=dark_css)
with demo:
gr.Markdown("#Hallucination Detector", elem_id="header_text")
gr.Markdown(
"Detects **intrinsic** (internal contradictions) and **extrinsic** "
"(source unsupported) hallucinations",
elem_id="header_text"
)
gen = gr.Textbox(lines=8, label="Generated Text")
src = gr.Textbox(lines=8, label="Source Text")
out = gr.JSON(label="🔍 Analysis Result (JSON)")
gen.submit(detect_hallucinations, inputs=[gen, src], outputs=out)
src.submit(detect_hallucinations, inputs=[gen, src], outputs=out)
gr.Button("Run Analysis").click(detect_hallucinations, inputs=[gen, src], outputs=out)
demo.launch()
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