import gradio as gr from huggingface_hub import InferenceClient from PyPDF2 import PdfReader import os # PDF 텍스트 미리 읽어오기 def extract_pdf_text(pdf_paths): full_text = "" for path in pdf_paths: reader = PdfReader(path) for page in reader.pages: text = page.extract_text() if text: full_text += text + "\n" return full_text.strip() # 미리 지정된 PDF 문서들 pdf_context = extract_pdf_text([ "assets/Programming-Fundamentals-1570222270.pdf", "assets/1분파이썬_강의자료_전체.pdf" ]) # Inference Client 설정 - 모델 변경됨 client = InferenceClient( model="mistralai/Mistral-7B-Instruct-v0.1", token=os.getenv("HUGGINGFACEHUB_API_TOKEN") # 반드시 등록 필요 ) def respond(message, history, system_message, max_tokens, temperature, top_p): messages = [{"role": "system", "content": system_message}] # history 기반 message 구성 for user_msg, bot_msg in history: if user_msg: messages.append({"role": "user", "content": user_msg}) if bot_msg: messages.append({"role": "assistant", "content": bot_msg}) # 문서 기반 질문 구성 messages.append({ "role": "user", "content": f"다음은 파이썬 프로그래밍 문서입니다:\n\n{pdf_context}\n\n질문: {message}" }) response = "" for chunk in client.chat_completion( messages=messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p, stream=True, ): delta = chunk.choices[0].delta.content if delta: response += delta yield response demo = gr.ChatInterface( fn=respond, additional_inputs=[ gr.Textbox(value="You are a helpful assistant answering based on the programming reference.", 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"), ], title="📘 파이썬 API 레퍼런스 챗봇 (Mistral 기반)", description="한국공대 수업자료 기반으로 질문에 답변하는 챗봇입니다." ) if __name__ == "__main__": demo.launch()