Create app.py

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+# languages and model
+LANGS = ["eng_Latn", "fra_Latn", "spa_Latn"]
+model_name = "facebook/nllb-200-distilled-600M"
+
+# model and tokinizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+device = 0 if torch.cuda.is_available() else -1
+
+# translate function
+def translate(input, src_lang, tgt_lang):
+  translation_pipeline = pipeline("translation", model=model, tokenizer=tokenizer, 
+                                  src_lang=src_lang, tgt_lang=tgt_lang,
+                                  max_length=400, device=device)
+  res = translation_pipeline(input)
+  return res[0]['translation_text']
+##
+##
+with gr.Blocks() as demo:
+    with gr.Row():
+      gr.Markdown("""
+                  > 👽 0xZee
+                  # 📝 NLP : Translation Demo
+                  > Demo #1 : **facebook/nllb-200-distilled-600M** Model (finetuned) 
+                  *Translation : English, French, Spanish*
+                  """)
+#
+    with gr.Row():
+          input = gr.Textbox(label="Text to translate", placeholder="Text here")
+    with gr.Row(): 
+          src = gr.Dropdown(label="Source Language", choices=LANGS)
+          tgt = gr.Dropdown(label="Target Language", choices=LANGS)
+    with gr.Row():
+          gr.Examples(examples=[["Building a better world, with sustainable energies", "eng_Latn", "spa_Latn"], ["Messi es el mejor del mundo, y de lejos", "spa_Latn", "fra_Latn"]], inputs=[input, src, tgt])
+    with gr.Row():
+          btn = gr.Button(value="Go Translate")  
+    with gr.Row():      
+          text_out = gr.Textbox(label="Translation")
+          btn.click(fn=translate, inputs=[input, src, tgt], outputs=text_out)
+
+
+
+if __name__ == "__main__":
+  demo.launch(debug=True)
+