Update app.py

app.py

@@ -1,28 +1,49 @@
 import gradio as gr
 import requests
+import numpy as np
 import os
 
+# إعدادات الموديل
 API_URL = "https://api-inference.huggingface.co/models/sentence-transformers/paraphrase-mpnet-base-v2"
-HF_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+HEADERS = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_TOKEN')}"}
 
 def get_embedding(text):
-    response = requests.post(
-        API_URL,
-        headers=headers,
-        json={"inputs": text}
-    )
+    response = requests.post(API_URL, headers=HEADERS, json={"inputs": text})
     if response.status_code == 200:
-        return response.json()
+        return np.array(response.json()[0])
     else:
-        return f"❌ Error: {response.status_code} - {response.text}"
+        return None
+
+def cosine_similarity(vec1, vec2):
+    if vec1 is None or vec2 is None:
+        return "❌ Error fetching embeddings"
+    dot = np.dot(vec1, vec2)
+    norm_a = np.linalg.norm(vec1)
+    norm_b = np.linalg.norm(vec2)
+    if norm_a == 0 or norm_b == 0:
+        return 0.0
+    return float(dot / (norm_a * norm_b))
+
+def compare_sentences(sentence1, sentence2):
+    embedding1 = get_embedding(sentence1)
+    embedding2 = get_embedding(sentence2)
+    similarity = cosine_similarity(embedding1, embedding2)
+    return {
+        "Sentence 1": sentence1,
+        "Sentence 2": sentence2,
+        "Cosine Similarity": round(similarity, 4) if isinstance(similarity, float) else similarity
+    }
 
+# واجهة Gradio
 iface = gr.Interface(
-    fn=get_embedding,
-    inputs=gr.Textbox(label="Enter text"),
+    fn=compare_sentences,
+    inputs=[
+        gr.Textbox(label="Sentence 1", placeholder="Enter the first sentence..."),
+        gr.Textbox(label="Sentence 2", placeholder="Enter the second sentence...")
+    ],
     outputs="json",
-    title="Text Embedding with paraphrase-mpnet-base-v2",
+    title="Sentence Similarity using HuggingFace API",
+    description="Compares two sentences using the `paraphrase-mpnet-base-v2` model and returns the cosine similarity."
 )
 
-iface.launch()
+iface.launch(share=True)