import gradio as gr
from transformers import pipeline

# 감성 분석 파이프라인 초기화
sentiment = pipeline("sentiment-analysis")

# 사용자 입력에 대한 감성 분석 결과를 반환하는 함수
def get_sentiment(입력):
    # 감성 분석 실행
    return sentiment(입력)


gr.Interface(fn=ask_question, inputs="입력", outputs="output", title="Sentiment Analysis", description="").launch()


# import gradio as gr
# from transformers import pipeline

# sentiment = pipeline("sentiment-analysis")

# def get_sentiment(입력):
#     # from transformers import AutoTokenizer, AutoModelForCausalLM
#     # model_name = "heegyu/koalpaca-355m"
#     # tokenizer = AutoTokenizer.from_pretrained(model_name)
#     # tokenizer.truncation_side = "right"
#     # model = AutoModelForCausalLM.from_pretrained(model_name)
#     return sentiment(입력)

# def get_response(output):
#     context = f"<usr>{context}\n<sys>"
#     inputs = tokenizer(
#         context, 
#         truncation=True,
#         max_length=512,
#         return_tensors="pt")
    
#     generation_args = dict(
#         max_length=256,
#         min_length=64,
#         eos_token_id=2,
#         do_sample=True,
#         top_p=1.0,
#         early_stopping=True
#     )

#     outputs = model.generate(**inputs, **generation_args)
#     response = tokenizer.decode(outputs[0])
#     print(context)
#     print(response)
#     response = response[len(context):].replace("</s>", "")

#     return response

# model, tokenizer = get_pipe()

# def ask_question(input_):
#     response = get_response(tokenizer, model, input_)
#     return response

# gr.Interface(fn=ask_question, inputs="text", outputs="text", title="KoAlpaca-355M", description="한국어로 질문하세요.").launch()