Delete app.py

app.py

@@ -1,680 +0,0 @@
-import spaces
-import json
-import subprocess
-import os
-from llama_cpp import Llama
-from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
-from llama_cpp_agent.providers import LlamaCppPythonProvider
-from llama_cpp_agent.chat_history import BasicChatHistory
-from llama_cpp_agent.chat_history.messages import Roles
-import gradio as gr
-from huggingface_hub import hf_hub_download
-import tempfile
-from typing import List, Tuple, Optional
-
-# PDF 처리 라이브러리 조건부 import
-try:
-    from docling.document_converter import DocumentConverter
-    DOCLING_AVAILABLE = True
-except ImportError:
-    DOCLING_AVAILABLE = False
-    print("Docling not available, using alternative PDF processing")
-    try:
-        import PyPDF2
-        import pdfplumber
-    except ImportError:
-        print("Warning: PDF processing libraries not fully installed")
-
-# 환경 변수에서 HF_TOKEN 가져오기
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-# 전역 변수 초기화 (중요!)
-llm = None
-llm_model = None
-document_context = ""  # PDF에서 추출한 문서 컨텍스트 저장
-document_filename = ""  # 현재 로드된 문서의 파일명
-
-print("Global variables initialized")
-print(f"document_context initial value: '{document_context}'")
-print(f"document_filename initial value: '{document_filename}'")
-
-# 모델 이름과 경로를 정의
-MISTRAL_MODEL_NAME = "Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503.gguf"
-
-# 모델 다운로드 (HF_TOKEN 사용)
-model_path = hf_hub_download(
-    repo_id="ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503",
-    filename=MISTRAL_MODEL_NAME,
-    local_dir="./models",
-    token=HF_TOKEN
-)
-
-print(f"Downloaded model path: {model_path}")
-
-css = """
-.bubble-wrap {
-    padding-top: calc(var(--spacing-xl) * 3) !important;
-}
-.message-row {
-    justify-content: space-evenly !important;
-    width: 100% !important;
-    max-width: 100% !important;
-    margin: calc(var(--spacing-xl)) 0 !important;
-    padding: 0 calc(var(--spacing-xl) * 3) !important;
-}
-.flex-wrap.user {
-    border-bottom-right-radius: var(--radius-lg) !important;
-}
-.flex-wrap.bot {
-    border-bottom-left-radius: var(--radius-lg) !important;
-}
-.message.user{
-    padding: 10px;
-}
-.message.bot{
-    text-align: right;
-    width: 100%;
-    padding: 10px;
-    border-radius: 10px;
-}
-.message-bubble-border {
-    border-radius: 6px !important;
-}
-.message-buttons {
-    justify-content: flex-end !important;
-}
-.message-buttons-left {
-    align-self: end !important;
-}
-.message-buttons-bot, .message-buttons-user {
-    right: 10px !important;
-    left: auto !important;
-    bottom: 2px !important;
-}
-.dark.message-bubble-border {
-    border-color: #343140 !important;
-}
-.dark.user {
-    background: #1e1c26 !important;
-}
-.dark.assistant.dark, .dark.pending.dark {
-    background: #16141c !important;
-}
-.upload-container {
-    margin-bottom: 20px;
-    padding: 15px;
-    border: 2px dashed #666;
-    border-radius: 10px;
-    background-color: #f0f0f0;
-}
-.dark .upload-container {
-    background-color: #292733;
-    border-color: #444;
-}
-"""
-
-def get_messages_formatter_type(model_name):
-    if "Mistral" in model_name or "BitSix" in model_name:
-        return MessagesFormatterType.MISTRAL  # CHATML 대신 MISTRAL 형식 사용
-    else:
-        raise ValueError(f"Unsupported model: {model_name}")
-
-@spaces.GPU
-def convert_pdf_to_markdown(file):
-    """Convert PDF file to Markdown"""
-    global document_context, document_filename
-    
-    if file is None:
-        return "No file uploaded.", {}
-    
-    try:
-        print(f"\n=== PDF Conversion Started ===")
-        print(f"File path: {file.name}")
-        
-        # DocumentConverter 인스턴스 생성
-        converter = DocumentConverter()
-        
-        # 파일 변환
-        result = converter.convert(file.name)
-        
-        # Markdown으로 내보내기
-        markdown_content = result.document.export_to_markdown()
-        
-        # 문서 컨텍스트 업데이트 (중요!)
-        document_context = markdown_content
-        document_filename = os.path.basename(file.name)
-        
-        # 메타데이터 추출
-        metadata = {
-            "filename": document_filename,
-            "conversion_status": "success",
-            "content_length": len(markdown_content),
-            "preview": markdown_content[:500] + "..." if len(markdown_content) > 500 else markdown_content
-        }
-        
-        print(f"✅ PDF conversion successful!")
-        print(f"📄 Filename: {document_filename}")
-        print(f"📏 Document length: {len(markdown_content)} characters")
-        print(f"📝 Document preview (first 300 chars):\n{markdown_content[:300]}...")
-        print(f"=== PDF Conversion Complete ===\n")
-        
-        # 전역 변수 확인 및 강제 설정
-        print(f"\n=== Before setting global variables ===")
-        print(f"global document_context length: {len(document_context)}")
-        print(f"global document_filename: {document_filename}")
-        
-        # globals() 함수를 사용하여 강제로 전역 변수 설정
-        globals()['document_context'] = markdown_content
-        globals()['document_filename'] = document_filename
-        
-        print(f"\n=== After setting global variables ===")
-        print(f"global document_context length: {len(globals()['document_context'])}")
-        print(f"global document_filename: {globals()['document_filename']}")
-        
-        return markdown_content, metadata
-        
-    except Exception as e:
-        error_msg = f"Error during PDF conversion: {str(e)}"
-        print(f"❌ {error_msg}")
-        document_context = ""
-        document_filename = ""
-        return error_msg, {"error": str(e)}
-
-def find_relevant_chunks(document, query, chunk_size=1500, overlap=300):
-    """Find relevant chunks from document based on query"""
-    if not document:
-        return ""
-    
-    print(f"Finding relevant chunks for query: {query}")
-    
-    # 간단한 키워드 기반 검색
-    query_words = query.lower().split()
-    chunks = []
-    
-    # 문서를 청크로 나누기
-    for i in range(0, len(document), chunk_size - overlap):
-        chunk = document[i:i + chunk_size]
-        chunks.append((i, chunk))
-    
-    print(f"Document split into {len(chunks)} chunks")
-    
-    # 각 청크의 관련성 점수 계산
-    scored_chunks = []
-    for idx, chunk in chunks:
-        chunk_lower = chunk.lower()
-        score = sum(1 for word in query_words if word in chunk_lower)
-        if score > 0:
-            scored_chunks.append((score, idx, chunk))
-    
-    # 상위 2개 청크 선택 (메모리 절약)
-    scored_chunks.sort(reverse=True, key=lambda x: x[0])
-    relevant_chunks = scored_chunks[:2]
-    
-    if relevant_chunks:
-        result = ""
-        for score, idx, chunk in relevant_chunks:
-            result += f"\n[Extracted from position {idx} - relevance score: {score}]\n{chunk}\n"
-        print(f"Found {len(relevant_chunks)} relevant chunks")
-        return result
-    else:
-        # 관련 청크를 찾지 못한 경우 문서 시작 부분 반환
-        print("No relevant chunks found, returning document beginning")
-        return document[:2000]
-
-@spaces.GPU(duration=120)
-def respond(
-    message,
-    history: list[dict],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    top_k,
-    repeat_penalty,
-):
-    global llm, llm_model
-    
-    # globals()를 사용하여 전역 변수에 접근
-    document_context = globals().get('document_context', '')
-    document_filename = globals().get('document_filename', '')
-    
-    # 디버깅을 위한 상세 로그
-    print(f"\n=== RESPOND Function Started ===")
-    print(f"User message: {message}")
-    print(f"Document context exists: {bool(document_context)}")
-    if document_context:
-        print(f"Document length: {len(document_context)}")
-        print(f"Document filename: {document_filename}")
-        print(f"Document preview (first 100 chars): {document_context[:100]}...")
-    else:
-        print("⚠️ document_context is empty!")
-        print(f"globals() keys (first 20): {list(globals().keys())[:20]}...")
-    
-    chat_template = get_messages_formatter_type(MISTRAL_MODEL_NAME)
-    
-    # 모델 파일 경로 확인
-    model_path_local = os.path.join("./models", MISTRAL_MODEL_NAME)
-    
-    if llm is None or llm_model != MISTRAL_MODEL_NAME:
-        print("Loading LLM model...")
-        llm = Llama(
-            model_path=model_path_local,
-            flash_attn=True,
-            n_gpu_layers=81,
-            n_batch=1024,
-            n_ctx=16384,  # 컨텍스트 크기
-            verbose=True  # 디버깅을 위한 상세 로그
-        )
-        llm_model = MISTRAL_MODEL_NAME
-        print("LLM model loaded successfully!")
-    
-    provider = LlamaCppPythonProvider(llm)
-    
-    # 기본 시스템 메시지 사용
-    system_prompt = system_message
-    
-    # 문서 컨텍스트가 있으면 시스템 메시지와 사용자 메시지 모두에 포함
-    if document_context and len(document_context) > 0:
-        doc_length = len(document_context)
-        print(f"📄 Including document context in message: {doc_length} characters")
-        
-        # 시스템 메시지에도 문서 정보 추가
-        system_prompt += f"\n\nCurrently loaded document: '{document_filename}'. You must reference this document content when answering all user questions."
-        
-        # 문서 내용을 적절한 크기로 제한
-        max_doc_length = 4000  # 최대 4000자로 제한
-        if doc_length > max_doc_length:
-            # 문서가 너무 긴 경우 처음과 끝 부분만 포함
-            doc_snippet = document_context[:2000] + "\n\n[... middle content omitted ...]\n\n" + document_context[-1500:]
-            enhanced_message = f"""Uploaded PDF document information:
-- Filename: {document_filename}
-- Document length: {doc_length} characters
-
-Document content (excerpt):
-{doc_snippet}
-
-User question: {message}
-
-Please answer the question based on the document above."""
-        else:
-            # 짧은 문서는 전체 포함
-            enhanced_message = f"""Uploaded PDF document information:
-- Filename: {document_filename}
-- Document length: {doc_length} characters
-
-Document content:
-{document_context}
-
-User question: {message}
-
-Please answer the question based on the document above."""
-        
-        print(f"Enhanced message length: {len(enhanced_message)}")
-        print(f"Message preview (first 300 chars):\n{enhanced_message[:300]}...")
-        
-        # 디버그: 최종 메시지 파일로 저장 (확인용)
-        with open("debug_last_message.txt", "w", encoding="utf-8") as f:
-            f.write(f"=== Debug Information ===\n")
-            f.write(f"Document length: {len(document_context)}\n")
-            f.write(f"Filename: {document_filename}\n")
-            f.write(f"User question: {message}\n")
-            f.write(f"\n=== Message to be sent ===\n")
-            f.write(enhanced_message)
-    else:
-        # 문서가 없는 경우
-        enhanced_message = message
-        if any(keyword in message.lower() for keyword in ["document", "pdf", "upload", "file", "content", "summary", "문서", "요약"]):
-            enhanced_message = f"{message}\n\n[System message: No PDF document is currently uploaded. Please upload a PDF file first.]"
-            print("Document-related question but no document loaded")
-        
-        # 디버그 메시지
-        print("⚠️ Warning: document_context is empty!")
-        print(f"document_context type: {type(document_context)}")
-        print(f"document_context value: {repr(document_context)}")
-        print(f"document_filename: {document_filename}")
-
-    settings = provider.get_provider_default_settings()
-    settings.temperature = temperature
-    settings.top_k = top_k
-    settings.top_p = top_p
-    settings.max_tokens = max_tokens
-    settings.repeat_penalty = repeat_penalty
-    settings.stream = True
-
-    # 시스템 프롬프트에 문서 내용 직접 포함 (문서가 있는 경우)
-    if document_context and len(document_context) > 0:
-        doc_snippet = document_context[:3000]  # 처음 3000자만 사용
-        enhanced_system_prompt = f"""{system_prompt}
-
-Currently loaded PDF document:
-Filename: {document_filename}
-Document content:
-{doc_snippet}
-{'' if len(document_context) <= 3000 else '... (remainder omitted)'}
-
-Answer user questions based on the document content above."""
-        
-        # 사용자 메시지는 단순하게
-        final_message = message
-    else:
-        enhanced_system_prompt = system_prompt
-        final_message = enhanced_message
-
-    agent = LlamaCppAgent(
-        provider,
-        system_prompt=enhanced_system_prompt,
-        predefined_messages_formatter_type=chat_template,
-        debug_output=True
-    )
-
-    messages = BasicChatHistory()
-
-    # 이전 대화 기록 추가 (수정됨)
-    for i in range(0, len(history)):
-        # 현재 메시지는 제외
-        if i < len(history) - 1 and history[i][1] is not None:
-            # 사용자 메시지
-            messages.add_message({
-                'role': Roles.user,
-                'content': history[i][0]
-            })
-            # 어시스턴트 메시지
-            messages.add_message({
-                'role': Roles.assistant,
-                'content': history[i][1]
-            })
-    
-    print(f"Sending final message: {final_message}")
-    
-    # 스트림 응답 생성
-    try:
-        stream = agent.get_chat_response(
-            final_message,  # 단순한 메시지 사용
-            llm_sampling_settings=settings,
-            chat_history=messages,
-            returns_streaming_generator=True,
-            print_output=False
-        )
-        
-        outputs = ""
-        for output in stream:
-            outputs += output
-            yield outputs
-    except Exception as e:
-        print(f"Error during stream generation: {e}")
-        yield "Sorry, an error occurred while generating the response. Please try again."
-
-def clear_document_context():
-    """Clear document context"""
-    global document_context, document_filename
-    document_context = ""
-    document_filename = ""
-    return "📭 Document context has been cleared. Please upload a new PDF."
-
-def check_document_status():
-    """Check current document status"""
-    global document_context, document_filename
-    print(f"\n=== Document Status Check ===")
-    print(f"document_context type: {type(document_context)}")
-    print(f"document_context length: {len(document_context) if document_context else 0}")
-    print(f"document_filename: '{document_filename}'")
-    
-    if document_context and len(document_context) > 0:
-        status = f"✅ Document loaded successfully.\n📄 Filename: {document_filename}\n📏 Document length: {len(document_context):,} characters"
-        print(f"Document first 100 chars: {document_context[:100]}")
-        return status
-    else:
-        return "📭 No document loaded. Please upload a PDF file."
-
-# Gradio 인터페이스 구성
-with gr.Blocks(theme=gr.themes.Soft(
-        primary_hue="blue",
-        secondary_hue="cyan",
-        neutral_hue="gray",
-        font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"]
-    ).set(
-        body_background_fill="#f8f9fa",
-        block_background_fill="#ffffff",
-        block_border_width="1px",
-        block_title_background_fill="#e9ecef",
-        input_background_fill="#ffffff",
-        button_secondary_background_fill="#e9ecef",
-        border_color_accent="#dee2e6",
-        border_color_primary="#ced4da",
-        background_fill_secondary="#f8f9fa",
-        color_accent_soft="transparent",
-        code_background_fill="#f1f3f5",
-    ), css=css) as demo:
-    
-    gr.Markdown("# On-Premise Optimized 'LLM+RAG Model' Service by VIDraft")
-    gr.Markdown("🔍 **Advanced document analysis with state-of-the-art language model for accurate Q&A based on your PDF content**")
-    gr.Markdown("🌐 **Supports both English and Korean languages seamlessly with context-aware responses**")
-    gr.Markdown("💡 **How to use**: 1) Upload PDF below → 2) Ask questions about the document → 3) Get AI-powered answers")
-    
-    # 채팅 인터페이스를 위쪽에 배치
-    with gr.Row():
-        with gr.Column():
-            # 채팅 인터페이스
-            chatbot = gr.Chatbot(elem_id="chatbot", height=500)
-            msg = gr.Textbox(
-                label="Message Input",
-                placeholder="Enter your question... (Upload a PDF to ask questions about its content)",
-                lines=2
-            )
-            with gr.Row():
-                submit = gr.Button("Send", variant="primary")
-                clear_chat = gr.Button("Clear Chat")
-    
-    # 예제를 중간에 배치 - 영어와 한국어 예제 모두 포함
-    gr.Examples(
-        examples=[
-            ["What is this document about?"],
-            ["Please summarize the main contents of the uploaded PDF document."],
-            ["What are the key dates or deadlines mentioned in the document?"],
-            ["What are the 3 most important key points in this document?"],
-            ["이 문서의 주요 내용을 요약해주세요."],
-            ["문서에 나온 중요한 일정이나 날짜를 알려주세요."],
-            ["이 문서에서 가장 중요한 3가지 핵심 포인트는 무엇인가요?"]
-        ],
-        inputs=msg
-    )
-    
-    # PDF 업로드 섹션을 아래쪽에 배치
-    with gr.Accordion("📄 PDF Document Upload", open=True):
-        with gr.Row():
-            with gr.Column(scale=1):
-                file_input = gr.File(
-                    label="Select PDF Document",
-                    file_types=[".pdf"],
-                    type="filepath"
-                )
-                with gr.Row():
-                    convert_button = gr.Button("Convert Document", variant="primary")
-                    clear_button = gr.Button("Clear Document", variant="secondary")
-                    test_button = gr.Button("Test Document", variant="secondary")
-                
-                status_text = gr.Textbox(
-                    label="Document Status", 
-                    interactive=False,
-                    value=check_document_status(),
-                    lines=3
-                )
-            
-            with gr.Column(scale=1):
-                with gr.Accordion("Converted Document Preview", open=False):
-                    converted_text = gr.Textbox(
-                        label="Markdown Conversion Result",
-                        lines=10,
-                        max_lines=20,
-                        interactive=False
-                    )
-                    metadata_output = gr.JSON(label="Metadata")
-    
-    # 고급 설정을 가장 아래에 배치
-    with gr.Accordion("⚙️ Advanced Settings", open=False):
-        system_message = gr.Textbox(
-            value="You are an AI assistant that can answer in both English and Korean. When a PDF document is provided, analyze its content accurately and provide detailed answers. Respond in the same language as the user's question.",
-            label="System Message",
-            lines=3
-        )
-        max_tokens = gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max Tokens")
-        temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.3, step=0.1, label="Temperature (lower = more consistent)")
-        top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.90, step=0.05, label="Top-p")
-        top_k = gr.Slider(minimum=0, maximum=100, value=40, step=1, label="Top-k")
-        repeat_penalty = gr.Slider(minimum=0.0, maximum=2.0, value=1.1, step=0.1, label="Repetition penalty")
-    
-    # 이벤트 핸들러
-    def user_submit(message, history):
-        return "", history + [[message, None]]
-    
-    def bot_response(history, system_msg, max_tok, temp, top_p_val, top_k_val, rep_pen):
-        if history and history[-1][1] is None:
-            user_message = history[-1][0]
-            
-            # 디버깅: 문서 컨텍스트 상태 확인
-            global document_context, document_filename
-            print(f"\n=== BOT RESPONSE Started ===")
-            print(f"User message: {user_message}")
-            if document_context:
-                print(f"📄 Document context active: {document_filename} ({len(document_context)} chars)")
-                print(f"Document preview (first 200 chars): {document_context[:200]}...")
-            else:
-                print("📭 No document context")
-            
-            # 단순한 형식 사용 - [user_message, assistant_message]
-            previous_history = []
-            for i in range(len(history) - 1):
-                if history[i][1] is not None:
-                    previous_history.append({
-                        "user": history[i][0],
-                        "assistant": history[i][1]
-                    })
-            
-            print(f"Previous conversations: {len(previous_history)}")
-            
-            # 문서가 있는 경우 특별 처리
-            if document_context and len(document_context) > 0:
-                print(f"📄 Generating document-based response... (Document length: {len(document_context)})")
-            
-            bot_message = ""
-            try:
-                for token in respond(
-                    user_message,
-                    previous_history,
-                    system_msg,
-                    max_tok,
-                    temp,
-                    top_p_val,
-                    top_k_val,
-                    rep_pen
-                ):
-                    bot_message = token
-                    history[-1][1] = bot_message
-                    yield history
-            except Exception as e:
-                print(f"❌ Error during response generation: {e}")
-                import traceback
-                traceback.print_exc()
-                history[-1][1] = "Sorry, an error occurred while generating the response. Please try again."
-                yield history
-    
-    # PDF 변환 이벤트
-    def on_pdf_convert(file):
-        """PDF conversion and status update"""
-        global document_context, document_filename
-        
-        if file is None:
-            return "", {}, "❌ No file selected."
-        
-        markdown_content, metadata = convert_pdf_to_markdown(file)
-        
-        if "error" in metadata:
-            status = f"❌ Conversion failed: {metadata['error']}"
-        else:
-            # 전역 변수 다시 한번 확인 및 설정 (globals() 사용)
-            globals()['document_context'] = markdown_content
-            globals()['document_filename'] = metadata['filename']
-            
-            status = f"✅ PDF document converted successfully!\n📄 Filename: {metadata['filename']}\n📏 Document length: {metadata['content_length']:,} characters\n\nYou can now ask questions about the document content in English or Korean.\n\nExample questions:\n- What is the main topic of this document?\n- Summarize the key points\n- 이 문서의 핵심 내용을 설명해주세요"
-            
-            print(f"\n✅ Document loading confirmed:")
-            print(f"- globals()['document_context'] length: {len(globals()['document_context'])}")
-            print(f"- globals()['document_filename']: {globals()['document_filename']}")
-            
-            # 최종 확인
-            if len(globals()['document_context']) > 0:
-                print("✅ Document successfully saved to global variables!")
-            else:
-                print("❌ Warning: Document not saved to global variables!")
-        
-        return markdown_content, metadata, status
-    
-    # 파일 업로드 시 자동 변환
-    file_input.change(
-        fn=on_pdf_convert,
-        inputs=[file_input],
-        outputs=[converted_text, metadata_output, status_text]
-    )
-    
-    # 수동 변환 버튼
-    convert_button.click(
-        fn=on_pdf_convert,
-        inputs=[file_input],
-        outputs=[converted_text, metadata_output, status_text]
-    )
-    
-    # 문서 테스트 함수
-    def test_document():
-        """Test currently loaded document"""
-        global document_context, document_filename
-        if document_context:
-            test_msg = f"✅ Document test results:\n"
-            test_msg += f"📄 Filename: {document_filename}\n"
-            test_msg += f"📏 Total length: {len(document_context):,} characters\n"
-            test_msg += f"📝 First 500 characters:\n{document_context[:500]}..."
-            return test_msg
-        else:
-            return "❌ No document currently loaded."
-    
-    test_button.click(
-        fn=test_document,
-        outputs=[status_text]
-    )
-    
-    clear_button.click(
-        fn=clear_document_context,
-        outputs=[status_text]
-    ).then(
-        fn=lambda: ("", {}, check_document_status()),
-        outputs=[converted_text, metadata_output, status_text]
-    )
-    
-    # 채팅 이벤트
-    msg.submit(user_submit, [msg, chatbot], [msg, chatbot]).then(
-        bot_response, 
-        [chatbot, system_message, max_tokens, temperature, top_p, top_k, repeat_penalty], 
-        chatbot
-    )
-    
-    submit.click(user_submit, [msg, chatbot], [msg, chatbot]).then(
-        bot_response, 
-        [chatbot, system_message, max_tokens, temperature, top_p, top_k, repeat_penalty], 
-        chatbot
-    )
-    
-    clear_chat.click(lambda: [], None, chatbot)
-
-if __name__ == "__main__":
-    # 필요한 디렉토리 생성
-    os.makedirs("./models", exist_ok=True)
-    
-    # 환경 변수 확인
-    if not HF_TOKEN:
-        print("⚠️  Warning: HF_TOKEN not set. Model download may be restricted.")
-        print("To set environment variable: export HF_TOKEN='your_huggingface_token'")
-    
-    demo.launch(
-        server_name="0.0.0.0",  # Accessible from local network
-        server_port=7860,
-        share=False  # Disabled for on-premise environment
-    )