Initial space setup

README.md

@@ -0,0 +1,22 @@
+---
+title: Bayan Usuli BERT API
+emoji: 📚
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# Bayan Usuli BERT API
+
+Arabic Islamic Jurisprudence (Usul al-Fiqh) Embedding Model API.
+
+This space provides an API for the `MohJaf/bayan-usuli-bert` sentence-transformers model.
+
+## Features
+
+- Get text embeddings for Arabic jurisprudence texts
+- Compute semantic similarity between texts

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+from sentence_transformers import SentenceTransformer
+import numpy as np
+
+# Load the model
+model = SentenceTransformer("MohJaf/bayan-usuli-bert")
+
+def get_embeddings(text):
+    """Get embeddings for input text"""
+    if not text or not text.strip():
+        return {"error": "No input text provided"}
+
+    embeddings = model.encode(text)
+    return {
+        "query": text,
+        "embeddings": embeddings.tolist(),
+        "dimensions": len(embeddings)
+    }
+
+def compute_similarity(text1, text2):
+    """Compute similarity between two texts"""
+    if not text1 or not text2:
+        return {"error": "Both texts are required"}
+
+    embeddings = model.encode([text1, text2])
+    similarity = float(np.dot(embeddings[0], embeddings[1]) /
+                       (np.linalg.norm(embeddings[0]) * np.linalg.norm(embeddings[1])))
+
+    return {
+        "text1": text1,
+        "text2": text2,
+        "similarity": similarity
+    }
+
+# Create Gradio interface
+with gr.Blocks(title="Bayan Usuli BERT API") as demo:
+    gr.Markdown("# Bayan Usuli BERT - Arabic Islamic Jurisprudence Model")
+    gr.Markdown("This API provides embeddings for Arabic texts related to Islamic jurisprudence (Usul al-Fiqh).")
+
+    with gr.Tab("Get Embeddings"):
+        text_input = gr.Textbox(label="Arabic Text", placeholder="Enter your text here...", rtl=True)
+        embed_btn = gr.Button("Get Embeddings")
+        embed_output = gr.JSON(label="Result")
+        embed_btn.click(get_embeddings, inputs=text_input, outputs=embed_output)
+
+    with gr.Tab("Compute Similarity"):
+        text1 = gr.Textbox(label="First Text", placeholder="Enter first text...", rtl=True)
+        text2 = gr.Textbox(label="Second Text", placeholder="Enter second text...", rtl=True)
+        sim_btn = gr.Button("Compute Similarity")
+        sim_output = gr.JSON(label="Result")
+        sim_btn.click(compute_similarity, inputs=[text1, text2], outputs=sim_output)
+
+demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+gradio>=4.0.0
+sentence-transformers>=2.2.0
+torch
+numpy