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app.py

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+import numpy as np
+import gradio as gr
+from sentence_transformers import SentenceTransformer, util
+
+# Load your SentenceTransformer model fine-tuned for NLI
+model = SentenceTransformer("Omartificial-Intelligence-Space/Arabic-Nli-Matryoshka")
+
+# Define the labels for NLI
+labels = ["contradiction", "entailment", "neutral"]
+
+# Function to compute similarity and classify relationship
+def predict(sentence1, sentence2):
+    sentences = [sentence1, sentence2]
+    embeddings = model.encode(sentences)
+    
+    # Compute cosine similarity between the two sentences
+    similarity_score = util.pytorch_cos_sim(embeddings[0], embeddings[1])
+    
+    # Placeholder logic for NLI (needs to be replaced with actual model inference)
+    # This is just an example; in reality, you need a classifier trained for NLI
+    scores = np.random.rand(3)  # Replace this with actual model prediction logic
+    scores = scores / scores.sum()  # Normalize to sum to 1
+    
+    label_probs = {labels[i]: float(scores[i]) for i in range(len(labels))}
+    
+    return {
+        "Similarity Score": similarity_score.item(),
+        "Label Probabilities": label_probs
+    }
+
+# Define inputs and outputs for Gradio interface
+inputs = [
+    gr.inputs.Textbox(lines=2, placeholder="Enter the first sentence here...", label="Sentence 1"),
+    gr.inputs.Textbox(lines=2, placeholder="Enter the second sentence here...", label="Sentence 2")
+]
+
+outputs = [
+    gr.outputs.Textbox(label="Similarity Score"),
+    gr.outputs.Label(num_top_classes=3, label="Label Probabilities")
+]
+
+examples = [
+    ["يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.", "ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه"],
+    ["الشاب نائم بينما الأم تقود ابنتها إلى الحديقة", "ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه"]
+]
+
+# Create Gradio interface
+gr.Interface(
+    fn=predict,
+    title="Arabic Semantic Similarity and NLI with SentenceTransformers",
+    description="Compute the semantic similarity and classify the relationship between two Arabic sentences using a SentenceTransformer model.",
+    inputs=inputs,
+    examples=examples,
+    outputs=outputs,
+    cache_examples=False,
+    article="Author: Your Name. Model from Hugging Face Hub: Omartificial-Intelligence-Space/Arabic-Nli-Matryoshka",
+).launch(debug=True, enable_queue=True)

requirements.txt

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+gradio
+sentence-transformers
+numpy
+torch