Update app.py

app.py

@@ -1,56 +1,15 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
 import gradio as gr
+from transformers import pipeline
 
-model = PeftModel.from_pretrained("RAIJAY/7B_QA_68348")
-tokenizer = AutoTokenizer.from_pretrained("RAIJAY/7B_QA_68348")
+# Hugging Face에서 모델 로드
+qa_model = pipeline("question-answering", model="bert-large-uncased-whole-word-masking-finetuned-squad")
 
-prompt = """This is a discussion between a person and Hassan Kane, an entrepreneur. 
-person: What are you working on?
-Hassan: This new AI community building the future of Africa
-person: Where are you?
-Hassan: In Lagos for a week, then Paris or London.
-person: How's it going? 
-Hassan: Not bad.. Just trying to hit EV (escape velocity) with my startup
-person: """
+# Gradio 인터페이스 정의
+def question_answering(question):
+    answer = qa_model(question)["answer"]
+    return answer
 
-def my_split(s, seps):
-    res = [s]
-    for sep in seps:
-        s, res = res, []
-        for seq in s:
-            res += seq.split(sep)
-    return res
+iface = gr.Interface(fn=question_answering, inputs="text", outputs="text")
 
-# input = "Who are you?"
-def chat_base(input):
-  p = prompt + input
-  input_ids = tokenizer(p, return_tensors="pt").input_ids
-  gen_tokens = model.generate(input_ids, do_sample=True, temperature=0.7, max_length=150,)
-  gen_text = tokenizer.batch_decode(gen_tokens)[0]
-  # print(gen_text)
-  result = gen_text[len(p):]   
-  # print(">", result)
-  result = my_split(result, [']', '\n'])[1]
-  # print(">>", result)
-  if "Hassan: " in result:
-   result = result.split("Hassan: ")[-1]
-# print(">>>", result)
-  return result
-  
-import gradio as gr
-
-def chat(message):
-    history = gr.get_state() or []
-    print(history)
-    response = chat_base(message)
-    history.append((message, response))
-    gr.set_state(history)
-    html = "<div class='chatbot'>"
-    for user_msg, resp_msg in history:
-        html += f"<div class='user_msg'>{user_msg}</div>"
-        html += f"<div class='resp_msg'>{resp_msg}</div>"
-    html += "</div>"
-    return response
-
-iface = gr.Interface(chat_base, gr.inputs.Textbox(label="Ask Hassan a Question"), "text", allow_screenshot=False, allow_flagging=False)
-iface.launch()
+# Gradio 앱 실행
+iface.launch()