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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from sentence_transformers import SentenceTransformer, util
import torch
import torch.nn.functional as F
# Load SBERT model
sbert_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
# Load NLI model
nli_model_name = "tasksource/ModernBERT-base-nli"
nli_tokenizer = AutoTokenizer.from_pretrained(nli_model_name)
nli_model = AutoModelForSequenceClassification.from_pretrained(nli_model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
nli_model.to(device)
# SBERT function
def compute_similarity(text1, text2):
embeddings = sbert_model.encode([text1, text2], convert_to_tensor=True)
similarity = float(util.pytorch_cos_sim(embeddings[0], embeddings[1])[0])
interpretation = ""
if similarity > 0.9:
interpretation = "🟢 Very High Similarity"
elif similarity > 0.75:
interpretation = "🟡 Moderate Similarity"
elif similarity > 0.5:
interpretation = "🟠 Low Similarity"
else:
interpretation = "🔴 Very Low Similarity"
return round(similarity, 4), interpretation
# NLI function
def check_entail(premise, hypothesis):
inputs = nli_tokenizer(premise, hypothesis, return_tensors="pt", truncation=True, padding=True, max_length=512).to(device)
with torch.no_grad():
logits = nli_model(**inputs).logits
probs = torch.softmax(logits, dim=-1)[0]
label = ["entailment", "neutral", "contradiction"][torch.argmax(probs).item()]
return label, { "entailment": float(probs[0]), "neutral": float(probs[1]), "contradiction": float(probs[2]) }
def run_bi_direction(a, b):
res1 = check_entail(a, b)
res2 = check_entail(b, a)
return res1[0], res1[1], res2[0], res2[1]
# Build the interface
with gr.Blocks() as demo:
gr.Markdown("# ✍️ Essay Comparison Tool")
with gr.Tab("Semantic Similarity (SBERT)"):
a1 = gr.Textbox(label="Essay A", lines=8)
b1 = gr.Textbox(label="Essay B", lines=8)
sim_button = gr.Button("Compare Similarity")
sim_score = gr.Textbox(label="Cosine Similarity (0–1)")
sim_interpret = gr.Textbox(label="Interpretation")
sim_button.click(fn=compute_similarity, inputs=[a1, b1], outputs=[sim_score, sim_interpret])
with gr.Tab("Bidirectional Entailment (NLI)"):
a2 = gr.Textbox(label="Essay A (Original)", lines=8)
b2 = gr.Textbox(label="Essay B (Modified)", lines=8)
nli_button = gr.Button("Run Entailment Check")
ab_label = gr.Textbox(label="A ➜ B Label")
ab_scores = gr.JSON(label="A ➜ B Scores")
ba_label = gr.Textbox(label="B ➜ A Label")
ba_scores = gr.JSON(label="B ➜ A Scores")
nli_button.click(fn=run_bi_direction, inputs=[a2, b2], outputs=[ab_label, ab_scores, ba_label, ba_scores])
if __name__ == "__main__":
demo.launch()
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