revised

app.py

@@ -1,61 +1,36 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("Kyudan/opus-mt-en-ro-finetuned-en-to-ro")
-
+from transformers import AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def respond(text):
+    model = AutoModelForSeq2SeqLM.from_pretrained("Kyudan/opus-mt-en-ro-finetuned-en-to-ro")
+    model.eval()
+    
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    # 토크나이저 로드
+    tokenizer = AutoTokenizer.from_pretrained("Kyudan/opus-mt-en-ro-finetuned-en-to-ro")
 
-    messages.append({"role": "user", "content": message})
+    # 번역할 입력 텍스트
+    text = text
 
-    response = ""
+    # 입력 텍스트를 토큰화
+    inputs = tokenizer.encode(text, return_tensors="pt")
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    # 모델을 사용하여 번역
+    outputs = model.generate(inputs)
 
-        response += token
-        yield response
+    # 번역된 텍스트를 디코딩
+    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
+    return translated_text
 """
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
 """
 demo = gr.ChatInterface(
     respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+    title = "translate English to Romanian"
 )
 
 

requirements.txt

@@ -1 +1,4 @@
-huggingface_hub==0.22.2
+huggingface_hub==0.22.2
+datasets
+transformers[sentencepiece]
+sacrebleu