Create app.py

app.py

@@ -0,0 +1,47 @@
+# Import our modules 
+import gradio as gr
+from turjuman import turjuman 
+import logging
+import os
+from transformers import AutoTokenizer
+
+
+logging.basicConfig(
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=os.environ.get("LOGLEVEL", "INFO").upper(),
+)
+logger = logging.getLogger("turjuman.translate")
+cache_dir="/content/mycache"
+
+
+# Get the turjuman object and its tokenizer
+turj = turjuman.turjuman(logger, cache_dir)
+tokenizer = AutoTokenizer.from_pretrained('UBC-NLP/AraT5-base-title-generation')
+
+
+# The translate function
+def translate(sent):
+    beam_options = {"search_method":"beam", "seq_length": 300, "num_beams":5, "no_repeat_ngram_size":2, "max_outputs":1}
+    targets = turj.translate(sent,**beam_options)
+    #print(targets)
+    ans = ""
+    for target in targets:
+        target = tokenizer.decode(target, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+        ans += target
+    return ans
+
+#print(translate('Здравствуй, друг'))
+
+interface = gr.Interface(fn=translate, inputs=['text'], outputs=['text'],
+             allow_flagging=False,
+             title='Turjuman Multi-lingual Translation',
+             description='Please write the sentence you are trying to translate',
+             css="""
+             .chatbox{display:flex; flex-direction-column}
+             .user_msg, resp_msg {padding: 4px;margin-bottom:4px;border-radius:4px; width:80%}
+             .user_msg {background-color:cornflowerblue; color:white; align-self:start}
+             .resp_msg {background-color:lightgray; align-self: self-end}
+             """)
+
+interface.launch()