Create app.py

app.py

@@ -0,0 +1,54 @@
+import torch
+import gradio as gr
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+from googletrans import Translator
+
+translator = Translator()
+tokenizer = GPT2Tokenizer.from_pretrained("sberbank-ai/mGPT-armenian")
+model = GPT2LMHeadModel.from_pretrained("sberbank-ai/mGPT-armenian")
+#model.cuda()
+#model.eval()
+
+description = "Multilingual generation with mGPT"
+title = "Generate your own example"
+
+examples = [["""English: The vase with flowers is on the table.\nFinnish translation:""", "In May we celebrate "]]
+
+article = (
+    "<p style='text-align: center'>"
+    "<a href='https://github.com/ai-forever/mgpt'>GitHub</a>  "
+    "</p>"
+)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+fp16 = device != 'cpu'
+
+def transl(text, src='en', dest='hy' ):
+    return translator.translate(text, src=src, dest=dest).text
+    
+def generate(prompt: str):
+            input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
+            out = model.generate(input_ids, 
+                    min_length=300, 
+                    max_length=700, 
+                    top_p=0.95, 
+                    top_k=0,
+                    temperature=0.9,
+                    no_repeat_ngram_size=5
+            )
+            generated_text = list(map(tokenizer.decode, out))[0]
+            return generated_text + '\n\n'+transl(generated_text, src='hy', dest='en')
+            
+
+interface = gr.Interface.load("huggingface/sberbank-ai/mGPT",
+            description=description,
+            examples=examples, 
+            fn=generate,
+            inputs="text",
+            outputs='text',
+            thumbnail = 'https://habrastorage.org/r/w1560/getpro/habr/upload_files/26a/fa1/3e1/26afa13e1d1a56f54c7b0356761af7b8.png',
+            theme = "peach",
+            article = article
+)
+
+interface.launch(enable_queue=True)