Create app.py

app.py

@@ -0,0 +1,42 @@
+#!pip install gradio
+import os
+import torch
+import gradio as gr
+import time
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+def translation(text):
+
+    model_checkpoint = "bigscience/mt0-base"
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+    #inference
+    inputs = tokenizer("translate to darija : " + text, return_tensors="pt")
+    output = model.generate(**inputs)
+    output = tokenizer.decode(output.cpu().numpy()[0], skip_special_tokens=True)
+
+    return output
+
+
+if __name__ == '__main__':
+    print('\tinit models')
+
+    #inputs = [gr.inputs.Radio(['nllb-distilled-600M', 'nllb-1.3B', 'nllb-distilled-1.3B'], label='NLLB Model'),
+    inputs = [gr.inputs.Textbox(lines=5, label="Input text")]
+
+    outputs = gr.outputs.Textbox(label="Input text")
+
+    title = "NLP Translation model from english to darija"
+
+    demo_status = "Demo is running on CPU"
+    description = f"Details: https://github.com/facebookresearch/fairseq/tree/nllb. {demo_status}"
+    examples = [
+    ['English', 'Darija', 'Hi nice to meet you']
+    ]
+
+    gr.Interface(translation,
+                 inputs,
+                 outputs,
+                 title=title,
+                 description=description,
+                 ).launch()