Add application file

app.py

@@ -0,0 +1,67 @@
+# 모델 로딩
+import torch
+from peft import PeftConfig, PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+base_model_name = "facebook/opt-350m"
+adapter_model_name = 'msy127/opt-350m-aihubqa-130-dpo-adapter'
+
+model = AutoModelForCausalLM.from_pretrained(base_model_name)
+model = PeftModel.from_pretrained(model, adapter_model_name).to(device)
+tokenizer = AutoTokenizer.from_pretrained(adapter_model_name)
+
+# 대화 누적 함수 (history) - prompt 자리에 history가 들어감 -> dialoGPT는 모델 집어넣기 전에 인코딩을 했었는데 OPENAI는 인코딩을 안한다.
+
+def predict(input, history):
+    history.append({"role": "user", "content": input})
+
+    # 일반모델
+    prompt = f"An AI tool that looks at the context and question separated by triple backquotes, finds the answer corresponding to the question in the context, and answers clearly.\n### Input: ```{input}```\n ### Output: "
+    inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
+    outputs = model.generate(input_ids=inputs, max_length=256)
+    generated_text = tokenizer.decode(outputs[0])
+    start_idx = len(prompt) + len('</s>')
+    stop_first_idx = generated_text.find("### Input:") # 첫 번째 "### Input:"을 찾습니다.
+    stop_idx = generated_text.find("### Input:", stop_first_idx + 1) # 첫 번째 "### Input:" 이후의 문자열에서 다시 "### Input:"을 찾습니다.
+    # print(start_idx , stop_idx)
+    # print(generated_text)
+    if stop_idx != -1:
+        response = generated_text[start_idx:stop_idx] # prompt 뒤에 있는 새롭게 생성된 텍스트만 ("### Input:" 전까지) 가져옵니다.
+
+    # 누적
+    history.append({"role": "assistant", "content": response})
+    # messages = [(history[i]["content"], history[i+1]["content"]) for i in range(1, len(history), 2)]
+    messages = [(history[i]["content"], history[i+1]["content"]) for i in range(0, len(history) - 1, 2)]
+
+    return messages, history
+
+
+# Gradio 인터페이스 설정
+import gradio as gr
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(label="ChatBot")
+
+    state = gr.State([
+        {"role": "system", "content": "당신은 친절한 인공지능 챗봇입니다. 입력에 대해 짧고 간결하고 친절하게 대답해주세요."}])
+
+    with gr.Row():
+        txt = gr.Textbox(show_label=False, placeholder="챗봇에게 아무거나 물어보세요").style(container=False)
+        # txt.submit(predict, [txt, state], [chatbot, state])
+
+    txt.submit(predict, [txt, state], [chatbot, state])
+
+# demo.launch(debug=True, share=True)
+demo.launch()
+
+
+# from PIL import Image
+# import gradio as gr
+# interface = gr.Interface(
+#     fn=classify_image,
+#     inputs=gr.components.Image(type="pil", label="Upload an Image"),
+#     outputs="text",
+#     live=True
+# )
+# interface.launch()