Create new file

app.py

@@ -0,0 +1,48 @@
+from transformers import BertTokenizer, EncoderDecoderModel
+import gradio as gr
+
+tokenizerM = BertTokenizer.from_pretrained("mareloraby/BERTShared-PoetryGen-arV01")
+bertSharedM = EncoderDecoderModel.from_pretrained("mareloraby/BERTShared-PoetryGen-arV01")
+# bertSharedM.cuda()
+
+
+def generate_response(text, k = 70, p = 0.9, nb = 4):
+  prompt = f"{text}"
+  encoded_prompt = tokenizerM.encode_plus(prompt, return_tensors = 'pt')#.to(device)
+  gneration = bertSharedM.generate(
+                            input_ids = encoded_prompt.input_ids, 
+                            attention_mask = encoded_prompt.attention_mask, 
+                            do_sample = True,
+                            top_k= k, 
+                            top_p = p,
+                            num_beams= nb,
+                            max_length =130, 
+                            repetition_penalty = 2.0,  
+                            no_repeat_ngram_size = 2,
+                            early_stopping=True)
+                    
+  generated_text = tokenizerM.decode(gneration[0], skip_special_tokens=True) 
+  bayts = generated_text.split("[BSEP]")
+  while("BSEP" not in bayts[-1]):
+    bayts = bayts[:-1]
+  bayts = bayts[:-1]
+  temp_poem = ''
+  for b in range(len(bayts)):
+      temp_line = bayts[b].split('[FSEP]')
+      temp_poem = temp_poem + temp_line[1] + ' - ' + temp_line[0] +'\n'
+
+  return temp_poem
+
+iface = gr.Interface(fn=generate_response,
+             title = 'BERTShared - topic based generation',
+          
+              inputs=[
+          gr.inputs.Radio(['حزينه','هجاء','عتاب','غزل','مدح','رومنسيه','دينية','وطنيه'],label='Choose Topic'),
+          gr.inputs.Slider(10, 200, step=10,default = 70, label='Top-K'),
+          gr.inputs.Slider(0.10, 0.99, step=0.02, default = 0.90, label='Top-P'),
+          gr.inputs.Slider(1, 20, step=1, default = 4, label='Beams'),
+
+          ],
+             outputs="text")
+             
+iface.launch()