Upload app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+import torch
+
+LANGS = ["kin_Latn","eng_Latn"]
+TASK = "translation"
+# CKPT = "DigitalUmuganda/Finetuned-NLLB"
+MODELS = ["facebook/nllb-200-distilled-600M","DigitalUmuganda/Finetuned-NLLB"]
+# model = AutoModelForSeq2SeqLM.from_pretrained(CKPT)
+# tokenizer = AutoTokenizer.from_pretrained(CKPT)
+
+device = 0 if torch.cuda.is_available() else -1
+
+du_model = AutoModelForSeq2SeqLM.from_pretrained("DigitalUmuganda/Finetuned-NLLB")
+fb_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
+models = {"facebook/nllb-200-distilled-600M":fb_model,"DigitalUmuganda/Finetuned-NLLB":du_model}
+tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+# def translate(text, src_lang, tgt_lang, max_length=400):
+def translate(CKPT,text, src_lang, tgt_lang, max_length=400):
+
+    """
+    Translate the text from source lang to target lang
+    """
+
+
+    translation_pipeline = pipeline(TASK,
+                                    model=models[CKPT],
+                                    tokenizer=tokenizer,
+                                    src_lang=src_lang,
+                                    tgt_lang=tgt_lang,
+                                    max_length=max_length,
+                                    device=device)
+
+    result = translation_pipeline(text)
+    return result[0]['translation_text']
+
+
+gr.Interface(
+    translate,
+    [
+        gr.components.Dropdown(label="choose a model",choices=MODELS)
+        gr.components.Textbox(label="Text"),
+        gr.components.Dropdown(label="Source Language", choices=LANGS),
+        gr.components.Dropdown(label="Target Language", choices=LANGS),
+        #gr.components.Slider(8, 512, value=400, step=8, label="Max Length")
+    ],
+    ["text"],
+    #examples=examples,
+    # article=article,
+    cache_examples=False,
+    title="Finetuned-NLLB-1",
+    #description=description
+).launch()
+