Update app.py

app.py

@@ -1,42 +1,69 @@
 import gradio as gr
+from transformers import pipeline
 
-def get_pipe():
-    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 model, tokenizer
-
-def get_response(tokenizer, model, context):
-    context = f"<usr>{context}\n<sys>"
-    inputs = tokenizer(
-        context, 
-        truncation=True,
-        max_length=512,
-        return_tensors="pt")
+# Sentiment analysis pipeline 생성
+sentiment = pipeline("sentiment-analysis")
+
+def get_sentiment(입력):
+    # 텍스트의 감성 분석을 수행
+    result = sentiment_analysis(입력)
+    # 분석 결과 반환
+    return result
+
+# Gradio 인터페이스 설정
+interface = gr.Interface(
+    fn=get_sentiment,  # 호출될 함수
+    inputs=gr.inputs.Textbox(lines=2, placeholder="여기에 텍스트를 입력하세요..."),  # 입력란 설정
+    outputs="text",  # 출력 형식
+    title="텍스트 감성 분석",  # UI 제목
+    description="이 앱은 입력된 텍스트의 감성을 분석합니다. 긍정적이거나 부정적인 결과를 보여줍니다.",  # UI 설명
+    examples=[["이 제품은 정말 좋습니다!"], ["제 기대에 못 미쳤어요."]],  # 예시 입력
+    theme="default",  # UI 테마
+    layout="vertical"  # UI 레이아웃
+)
+
+# Gradio 앱 실행
+interface.launch()
+
+# import gradio as gr
+
+# def get_pipe():
+#     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 model, tokenizer
+
+# def get_response(tokenizer, model, context):
+#     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()
+#     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()