Update app.py

app.py

@@ -33,9 +33,21 @@ joined_df = matn_info.merge(df[cols_to_use], left_index=True, right_on='__index_
 df = joined_df.copy()
 
 model = SentenceTransformer('FDSRashid/QulBERT', token=Secret_token)
-
+arr = np.array(df['embed'].to_list())
 
 def find_most_similar_matn(text, n):
     embed_text = model.encode(araby.strip_diacritics(text))
-    
+    cos_sim = cosine_similarity(embed_text.reshape(1, -1), arr)
+    indices = np.argsort(cos_sim)[0][-n:]
+    matns = df.iloc[indices]
+    matns['Similarity'] = cos_sim[0][indices]
+    return matns[['Book', 'matn', 'taraf_ID', 'Book ID', 'Hadith Number', 'Author', 'Similarity']]
+
+with gr.Blocks() as demo:
+    text_input = gr.Textbox()
+    num_hadith = gr.Slider(1, 50, value = 5, label = 'Num Hadith', info = 'Choose the number of Hadith to Return')
+    text_output = gr.DataFrame()
+    text_button = gr.Button("Retrieve")
+    text_button.click(find_most_similar_matn, inputs=[text_input, num_hadith], outputs=text_output)
+demo.launch()