first commit

app.py

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+import gradio as gr
+from load_model import pipe
+from utils import Translation
+
+
+
+def reply(message, history):
+    txt = Translation(message, "en")
+    if txt.original == "en":
+        response = pipe(message)
+        return response[0]["generated_text"].split("Answer\n")[1]
+    else:
+        translation = txt.translatef()
+        response = pipe(translation)
+        t = Translation(response[0]["generated_text"].split("Answer\n")[1], txt.original)
+        res = t.translatef()
+        return res
+
+
+demo = gr.ChatInterface(fn=reply, examples=["What is data science?", "你好你好吗？", "Ποιος ήταν ο Αρχιμήδης;", "महात्मा गांधी के बारे में बात करें", "Descrivi la miglior ricetta italiana", "Wo liegt die Mauer von Berlin?"], title="Multilingual-Phi Bot")
+demo.launch()

load_model.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
+
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=2048)

requirements.txt

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+transformers==4.39.3
+langdetect==1.0.9
+deep-translator==1.11.4

utils.py

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+from langdetect import detect
+from deep_translator import GoogleTranslator
+
+class Translation:
+    def __init__(self, text, destination):
+        self.text = text
+        self.destination = destination
+        try:
+            self.original = detect(self.text)
+        except Exception as e:
+            self.original = "auto"
+    def translatef(self):
+        translator = GoogleTranslator(source=self.original, target=self.destination)
+        translation = translator.translate(self.text)
+        return translation