update to facebook Bart50

main.py

@@ -2,18 +2,28 @@ import os
 from fastapi import FastAPI, Request
 from fastapi.staticfiles import StaticFiles
 from fastapi.responses import FileResponse
-from transformers import pipeline
+from transformers import pipeline, MBartForConditionalGeneration, MBart50TokenizerFast
 
 
+model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-one-to-many-mmt")
+tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-one-to-many-mmt", src_lang="en_XX")
+
 app = FastAPI()
 
 
-pipe_flan = pipeline("translation_en_to_ml", model="google/flan-t5-small")
 
 @app.get("/infer_t5")
 def t5(input):
-    output = pipe_flan(input)
-    return {"output": output[0]["generated_text"]}
+    model_inputs = tokenizer(input, return_tensors="pt")
+
+    # translate from English to Malayalam
+    generated_tokens = model.generate(
+        **model_inputs,
+        forced_bos_token_id=tokenizer.lang_code_to_id["ml_IN"]
+    )
+    
+    output = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+    return {"output":output}
 
 app.mount("/", StaticFiles(directory="static", html=True), name="static")
 

static/script.js

@@ -68,7 +68,7 @@ async function getMessage(){
             const [prompterText, assistantText] = generatePrompterAssistantText(data[0].generated_text);
             // const en_text_ml = "English: "  + assistantText[0] + " Malayalam:";
             // console.log(en_text_ml)
-            outPutElement.textContent = await translateText(assistantText);
+            outPutElement.textContent = await translateText(assistantText[0]);
             const pElement = document.createElement('p')
             pElement.textContent = inputElement.value
             pElement.addEventListener('click', () => changeInput(pElement.textContent))