app-backup.py

# ──────────────────────────────── Imports ────────────────────────────────
import os, json, re, logging, requests, markdown, time, io
from datetime import datetime

import streamlit as st
from openai import OpenAI  # OpenAI 라이브러리

from gradio_client import Client
import pandas as pd
import PyPDF2  # For handling PDF files

# ──────────────────────────────── Environment Variables / Constants ─────────────────────────
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")  
BRAVE_KEY      = os.getenv("SERPHOUSE_API_KEY", "")  # Keep this name
BRAVE_ENDPOINT = "https://api.search.brave.com/res/v1/web/search"
IMAGE_API_URL  = "http://211.233.58.201:7896"
MAX_TOKENS     = 7999

# Search modes and style definitions (in English)
SEARCH_MODES = {
    "comprehensive": "Comprehensive answer with multiple sources",
    "academic": "Academic and research-focused results",
    "news": "Latest news and current events",
    "technical": "Technical and specialized information",
    "educational": "Educational and learning resources"
}

RESPONSE_STYLES = {
    "professional": "Professional and formal tone",
    "casual": "Friendly and conversational tone",
    "simple": "Simple and easy to understand",
    "detailed": "Detailed and thorough explanations"
}

# Example search queries
EXAMPLE_QUERIES = {
    "example1": "What are the latest developments in quantum computing?",
    "example2": "How does climate change affect biodiversity in tropical rainforests?",
    "example3": "What are the economic implications of artificial intelligence in the job market?"
}

# ──────────────────────────────── Logging ────────────────────────────────
logging.basicConfig(level=logging.INFO,
                    format="%(asctime)s - %(levelname)s - %(message)s")

# ──────────────────────────────── OpenAI Client ──────────────────────────

# OpenAI 클라이언트에 타임아웃과 재시도 로직 추가
@st.cache_resource
def get_openai_client():
    """Create an OpenAI client with timeout and retry settings."""
    if not OPENAI_API_KEY:
        raise RuntimeError("⚠️ OPENAI_API_KEY 환경 변수가 설정되지 않았습니다.")
    return OpenAI(
        api_key=OPENAI_API_KEY,
        timeout=60.0,  # 타임아웃 60초로 설정
        max_retries=3  # 재시도 횟수 3회로 설정
    )
    
# ──────────────────────────────── System Prompt ─────────────────────────
def get_system_prompt(mode="comprehensive", style="professional", include_search_results=True, include_uploaded_files=False) -> str:
    """
    Generate a system prompt for the perplexity-like interface based on:
    - The selected search mode and style
    - Guidelines for using web search results and uploaded files
    """

    # Base prompt for comprehensive mode
    comprehensive_prompt = """
You are an advanced AI assistant that provides comprehensive answers with multiple sources, similar to Perplexity.

Your task is to:
1. Thoroughly analyze the user's query
2. Provide a clear, well-structured answer integrating information from multiple sources
3. Include relevant images, videos, and links in your response
4. Format your answer with proper headings, bullet points, and sections
5. Cite sources inline and provide a references section at the end

Important guidelines:
- Organize information logically with clear section headings
- Use bullet points and numbered lists for clarity
- Include specific, factual information whenever possible
- Provide balanced perspectives on controversial topics
- Display relevant statistics, data, or quotes when appropriate
- Format your response using markdown for readability
"""

    # Alternative modes
    mode_prompts = {
        "academic": """
Your focus is on providing academic and research-focused responses:
- Prioritize peer-reviewed research and academic sources
- Include citations in a formal academic format
- Discuss methodologies and research limitations where relevant
- Present different scholarly perspectives on the topic
- Use precise, technical language appropriate for an academic audience
""",
        "news": """
Your focus is on providing the latest news and current events:
- Prioritize recent news articles and current information
- Include publication dates for all news sources
- Present multiple perspectives from different news outlets
- Distinguish between facts and opinions/editorial content
- Update information with the most recent developments
""",
        "technical": """
Your focus is on providing technical and specialized information:
- Use precise technical terminology appropriate to the field
- Include code snippets, formulas, or technical diagrams where relevant
- Break down complex concepts into step-by-step explanations
- Reference technical documentation, standards, and best practices
- Consider different technical approaches or methodologies
""",
        "educational": """
Your focus is on providing educational and learning resources:
- Structure information in a learning-friendly progression
- Include examples, analogies, and visual explanations
- Highlight key concepts and definitions
- Suggest further learning resources at different difficulty levels
- Present information that's accessible to learners at various levels
"""
    }

    # Response styles
    style_guides = {
        "professional": "Use a professional, authoritative voice. Clearly explain technical terms and present data systematically.",
        "casual": "Use a relaxed, conversational style with a friendly tone. Include relatable examples and occasionally use informal expressions.",
        "simple": "Use straightforward language and avoid jargon. Keep sentences and paragraphs short. Explain concepts as if to someone with no background in the subject.",
        "detailed": "Provide thorough explanations with comprehensive background information. Explore nuances and edge cases. Present multiple perspectives and detailed analysis."
    }

    # Guidelines for using search results
    search_guide = """
Guidelines for Using Search Results:
- Include source links directly in your response using markdown: [Source Name](URL)
- For each major claim or piece of information, indicate its source
- If sources conflict, explain the different perspectives and their reliability
- Include 3-5 relevant images by writing: ![Image description](image_url)
- Include 1-2 relevant video links when appropriate by writing: [Video: Title](video_url)
- Format search information into a cohesive, well-structured response
- Include a "References" section at the end listing all major sources with links
"""

    # Guidelines for using uploaded files
    upload_guide = """
Guidelines for Using Uploaded Files:
- Treat the uploaded files as primary sources for your response
- Extract and highlight key information from files that directly addresses the query
- Quote relevant passages and cite the specific file
- For numerical data in CSV files, consider creating summary statements
- For PDF content, reference specific sections or pages
- Integrate file information seamlessly with web search results
- When information conflicts, prioritize file content over general web results
"""

    # Choose base prompt based on mode
    if mode == "comprehensive":
        final_prompt = comprehensive_prompt
    else:
        final_prompt = comprehensive_prompt + "\n" + mode_prompts.get(mode, "")

    # Add style guide
    if style in style_guides:
        final_prompt += f"\n\nTone and Style: {style_guides[style]}"

    # Add search results guidance
    if include_search_results:
        final_prompt += f"\n\n{search_guide}"

    # Add uploaded files guidance
    if include_uploaded_files:
        final_prompt += f"\n\n{upload_guide}"

    # Additional formatting instructions
    final_prompt += """
\n\nAdditional Formatting Requirements:
- Use markdown headings (## and ###) to organize your response 
- Use bold text (**text**) for emphasis on important points
- Include a "Related Questions" section at the end with 3-5 follow-up questions
- Format your response with proper spacing and paragraph breaks
- Make all links clickable by using proper markdown format: [text](url)
"""

    return final_prompt

# ──────────────────────────────── Brave Search API ────────────────────────
@st.cache_data(ttl=3600)
def brave_search(query: str, count: int = 20):
    """
    Call the Brave Web Search API → list[dict]
    Returns fields: index, title, link, snippet, displayed_link
    """
    if not BRAVE_KEY:
        raise RuntimeError("⚠️ SERPHOUSE_API_KEY (Brave API Key) environment variable is empty.")

    headers = {
        "Accept": "application/json",
        "Accept-Encoding": "gzip",
        "X-Subscription-Token": BRAVE_KEY
    }
    params = {"q": query, "count": str(count)}

    for attempt in range(3):
        try:
            r = requests.get(BRAVE_ENDPOINT, headers=headers, params=params, timeout=15)
            r.raise_for_status()
            data = r.json()

            logging.info(f"Brave search result data structure: {list(data.keys())}")

            raw = data.get("web", {}).get("results") or data.get("results", [])
            if not raw:
                logging.warning(f"No Brave search results found. Response: {data}")
                raise ValueError("No search results found.")
            
            arts = []
            for i, res in enumerate(raw[:count], 1):
                url = res.get("url", res.get("link", ""))
                host = re.sub(r"https?://(www\.)?", "", url).split("/")[0]
                arts.append({
                    "index": i,
                    "title": res.get("title", "No title"),
                    "link": url,
                    "snippet": res.get("description", res.get("text", "No snippet")),
                    "displayed_link": host
                })

            logging.info(f"Brave search success: {len(arts)} results")
            return arts

        except Exception as e:
            logging.error(f"Brave search failure (attempt {attempt+1}/3): {e}")
            if attempt < 2:
                time.sleep(2)

    return []

def mock_results(query: str) -> str:
    """Fallback search results if API fails"""
    ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    return (f"# Fallback Search Content (Generated: {ts})\n\n"
            f"The search API request failed. Please generate a response based on any pre-existing knowledge about '{query}'.\n\n"
            f"You may consider the following points:\n\n"
            f"- Basic concepts and importance of {query}\n"
            f"- Commonly known related statistics or trends\n"
            f"- Typical expert opinions on this subject\n"
            f"- Questions that readers might have\n\n"
            f"Note: This is fallback guidance, not real-time data.\n\n")

def do_web_search(query: str) -> str:
    """Perform web search and format the results."""
    try:
        arts = brave_search(query, 20)
        if not arts:
            logging.warning("No search results, using fallback content")
            return mock_results(query)

        hdr = "# Web Search Results\nUse these results to provide a comprehensive answer with multiple sources. Include relevant images, videos, and links.\n\n"
        body = "\n".join(
            f"### Result {a['index']}: {a['title']}\n\n{a['snippet']}\n\n"
            f"**Source**: [{a['displayed_link']}]({a['link']})\n\n---\n"
            for a in arts
        )
        return hdr + body
    except Exception as e:
        logging.error(f"Web search process failed: {str(e)}")
        return mock_results(query)

# ──────────────────────────────── File Upload Handling ─────────────────────
def process_text_file(file):
    """Handle text file"""
    try:
        content = file.read()
        file.seek(0)

        text = content.decode('utf-8', errors='ignore')
        if len(text) > 10000:
            text = text[:9700] + "...(truncated)..."

        result = f"## Text File: {file.name}\n\n"
        result += text
        return result
    except Exception as e:
        logging.error(f"Error processing text file: {str(e)}")
        return f"Error processing text file: {str(e)}"

def process_csv_file(file):
    """Handle CSV file"""
    try:
        content = file.read()
        file.seek(0)

        df = pd.read_csv(io.BytesIO(content))
        result = f"## CSV File: {file.name}\n\n"
        result += f"- Rows: {len(df)}\n"
        result += f"- Columns: {len(df.columns)}\n"
        result += f"- Column Names: {', '.join(df.columns.tolist())}\n\n"

        result += "### Data Preview\n\n"
        preview_df = df.head(10)
        try:
            markdown_table = preview_df.to_markdown(index=False)
            if markdown_table:
                result += markdown_table + "\n\n"
            else:
                result += "Unable to display CSV data.\n\n"
        except Exception as e:
            logging.error(f"Markdown table conversion error: {e}")
            result += "Displaying data as text:\n\n"
            result += str(preview_df) + "\n\n"

        num_cols = df.select_dtypes(include=['number']).columns
        if len(num_cols) > 0:
            result += "### Basic Statistical Information\n\n"
            try:
                stats_df = df[num_cols].describe().round(2)
                stats_markdown = stats_df.to_markdown()
                if stats_markdown:
                    result += stats_markdown + "\n\n"
                else:
                    result += "Unable to display statistical information.\n\n"
            except Exception as e:
                logging.error(f"Statistical info conversion error: {e}")
                result += "Unable to generate statistical information.\n\n"

        return result
    except Exception as e:
        logging.error(f"CSV file processing error: {str(e)}")
        return f"Error processing CSV file: {str(e)}"

def process_pdf_file(file):
    """Handle PDF file"""
    try:
        # Read file in bytes
        file_bytes = file.read()
        file.seek(0)

        # Use PyPDF2
        pdf_file = io.BytesIO(file_bytes)
        reader = PyPDF2.PdfReader(pdf_file, strict=False)

        # Basic info
        result = f"## PDF File: {file.name}\n\n"
        result += f"- Total pages: {len(reader.pages)}\n\n"

        # Extract text by page (limit to first 5 pages)
        max_pages = min(5, len(reader.pages))
        all_text = ""

        for i in range(max_pages):
            try:
                page = reader.pages[i]
                page_text = page.extract_text()

                current_page_text = f"### Page {i+1}\n\n"
                if page_text and len(page_text.strip()) > 0:
                    # Limit to 1500 characters per page
                    if len(page_text) > 1500:
                        current_page_text += page_text[:1500] + "...(truncated)...\n\n"
                    else:
                        current_page_text += page_text + "\n\n"
                else:
                    current_page_text += "(No text could be extracted from this page)\n\n"

                all_text += current_page_text

                # If total text is too long, break
                if len(all_text) > 8000:
                    all_text += "...(truncating remaining pages; PDF is too large)...\n\n"
                    break

            except Exception as page_err:
                logging.error(f"Error processing PDF page {i+1}: {str(page_err)}")
                all_text += f"### Page {i+1}\n\n(Error extracting content: {str(page_err)})\n\n"

        if len(reader.pages) > max_pages:
            all_text += f"\nNote: Only the first {max_pages} pages are shown out of {len(reader.pages)} total.\n\n"

        result += "### PDF Content\n\n" + all_text
        return result

    except Exception as e:
        logging.error(f"PDF file processing error: {str(e)}")
        return f"## PDF File: {file.name}\n\nError occurred: {str(e)}\n\nThis PDF file cannot be processed."

def process_uploaded_files(files):
    """Combine the contents of all uploaded files into one string."""
    if not files:
        return None

    result = "# Uploaded File Contents\n\n"
    result += "Below is the content from the files provided by the user. Integrate this data as a main source of information for your response.\n\n"

    for file in files:
        try:
            ext = file.name.split('.')[-1].lower()
            if ext == 'txt':
                result += process_text_file(file) + "\n\n---\n\n"
            elif ext == 'csv':
                result += process_csv_file(file) + "\n\n---\n\n"
            elif ext == 'pdf':
                result += process_pdf_file(file) + "\n\n---\n\n"
            else:
                result += f"### Unsupported File: {file.name}\n\n---\n\n"
        except Exception as e:
            logging.error(f"File processing error {file.name}: {e}")
            result += f"### File processing error: {file.name}\n\nError: {e}\n\n---\n\n"

    return result

# ──────────────────────────────── Image & Utility ─────────────────────────
def get_images_for_query(query, count=5):
    """
    Simulate getting relevant images for a query.
    In a real implementation, this would call an image search API.
    """
    # This is a placeholder - in production, you would use a real image search API
    sample_images = [
        "https://source.unsplash.com/random/800x600/?"+query.replace(" ", "+"),
        "https://source.unsplash.com/random/600x400/?"+query.replace(" ", "+"),
        "https://source.unsplash.com/random/400x300/?"+query.replace(" ", "+"),
    ]
    return sample_images[:min(count, len(sample_images))]

def get_videos_for_query(query, count=2):
    """
    Simulate getting relevant videos for a query.
    In a real implementation, this would call a video search API.
    """
    # This is a placeholder - in production, you would use a real video search API
    sample_videos = [
        {"title": f"Introduction to {query}", "url": "https://www.youtube.com/results?search_query="+query.replace(" ", "+")},
        {"title": f"Detailed explanation of {query}", "url": "https://www.youtube.com/results?search_query=advanced+"+query.replace(" ", "+")}
    ]
    return sample_videos[:min(count, len(sample_videos))]

def generate_image(prompt, w=768, h=768, g=3.5, steps=30, seed=3):
    """Image generation function."""
    if not prompt:
        return None, "Insufficient prompt"
    try:
        res = Client(IMAGE_API_URL).predict(
            prompt=prompt, width=w, height=h, guidance=g,
            inference_steps=steps, seed=seed,
            do_img2img=False, init_image=None,
            image2image_strength=0.8, resize_img=True,
            api_name="/generate_image"
        )
        return res[0], f"Seed: {res[1]}"
    except Exception as e:
        logging.error(e)
        return None, str(e)

def extract_image_prompt(response_text: str, topic: str):
    """
    Generate a single-line English image prompt from the response content.
    """
    client = get_openai_client()
    
    try:
        response = client.chat.completions.create(
            model="gpt-4.1-mini",
            messages=[
                {"role": "system", "content": "Generate a single-line English image prompt from the following text. Return only the prompt text, nothing else."},
                {"role": "user", "content": f"Topic: {topic}\n\n---\n{response_text}\n\n---"}
            ],
            temperature=1,
            max_tokens=80,
            top_p=1
        )
        
        return response.choices[0].message.content.strip()
    except Exception as e:
        logging.error(f"OpenAI image prompt generation error: {e}")
        return f"A professional photo related to {topic}, high quality"

def md_to_html(md: str, title="Perplexity-like Response"):
    """Convert Markdown to HTML."""
    return f"<!DOCTYPE html><html><head><title>{title}</title><meta charset='utf-8'></head><body>{markdown.markdown(md)}</body></html>"

def keywords(text: str, top=5):
    """Simple keyword extraction."""
    cleaned = re.sub(r"[^가-힣a-zA-Z0-9\s]", "", text)
    return " ".join(cleaned.split()[:top])

# ──────────────────────────────── Streamlit UI ────────────────────────────
def perplexity_app():
    st.title("Perplexity-like AI Assistant")

    # Set default session state
    if "ai_model" not in st.session_state:
        st.session_state.ai_model = "gpt-4.1-mini"  # 고정 모델 설정
    if "messages" not in st.session_state:
        st.session_state.messages = []
    if "auto_save" not in st.session_state:
        st.session_state.auto_save = True
    if "generate_image" not in st.session_state:
        st.session_state.generate_image = False
    if "web_search_enabled" not in st.session_state:
        st.session_state.web_search_enabled = True
    if "search_mode" not in st.session_state:
        st.session_state.search_mode = "comprehensive"
    if "response_style" not in st.session_state:
        st.session_state.response_style = "professional"

    # Sidebar UI
    sb = st.sidebar
    sb.title("Search Settings")
    
    sb.subheader("Response Configuration")
    sb.selectbox(
        "Search Mode", 
        options=list(SEARCH_MODES.keys()), 
        format_func=lambda x: SEARCH_MODES[x],
        key="search_mode"
    )
    
    sb.selectbox(
        "Response Style",
        options=list(RESPONSE_STYLES.keys()),
        format_func=lambda x: RESPONSE_STYLES[x],
        key="response_style"
    )
    
    # Example queries
    sb.subheader("Example Queries")
    c1, c2, c3 = sb.columns(3)
    if c1.button("Quantum Computing", key="ex1"):
        process_example(EXAMPLE_QUERIES["example1"])
    if c2.button("Climate Change", key="ex2"):
        process_example(EXAMPLE_QUERIES["example2"])
    if c3.button("AI Economics", key="ex3"):
        process_example(EXAMPLE_QUERIES["example3"])
    
    sb.subheader("Other Settings")
    sb.toggle("Auto Save", key="auto_save")
    sb.toggle("Auto Image Generation", key="generate_image")
    
    web_search_enabled = sb.toggle("Use Web Search", value=st.session_state.web_search_enabled)
    st.session_state.web_search_enabled = web_search_enabled
    
    if web_search_enabled:
        st.sidebar.info("✅ Web search results will be integrated into the response.")

    # Download the latest response
    latest_response = next(
        (m["content"] for m in reversed(st.session_state.messages) 
         if m["role"] == "assistant" and m["content"].strip()), 
        None
    )
    if latest_response:
        # Extract a title from the response - first heading or first line
        title_match = re.search(r"# (.*?)(\n|$)", latest_response)
        if title_match:
            title = title_match.group(1).strip()
        else:
            first_line = latest_response.split('\n', 1)[0].strip()
            title = first_line[:40] + "..." if len(first_line) > 40 else first_line
        
        sb.subheader("Download Latest Response")
        d1, d2 = sb.columns(2)
        d1.download_button("Download as Markdown", latest_response, 
                           file_name=f"{title}.md", mime="text/markdown")
        d2.download_button("Download as HTML", md_to_html(latest_response, title),
                           file_name=f"{title}.html", mime="text/html")

    # JSON conversation record upload
    up = sb.file_uploader("Load Conversation History (.json)", type=["json"], key="json_uploader")
    if up:
        try:
            st.session_state.messages = json.load(up)
            sb.success("Conversation history loaded successfully")
        except Exception as e:
            sb.error(f"Failed to load: {e}")

    # JSON conversation record download
    if sb.button("Download Conversation as JSON"):
        sb.download_button(
            "Save",
            data=json.dumps(st.session_state.messages, ensure_ascii=False, indent=2),
            file_name="conversation_history.json",
            mime="application/json"
        )

    # File Upload
    st.subheader("Upload Files")
    uploaded_files = st.file_uploader(
        "Upload files to be used as reference (txt, csv, pdf)",
        type=["txt", "csv", "pdf"],
        accept_multiple_files=True,
        key="file_uploader"
    )
    
    if uploaded_files:
        file_count = len(uploaded_files)
        st.success(f"{file_count} files uploaded. They will be used as sources for your query.")
        
        with st.expander("Preview Uploaded Files", expanded=False):
            for idx, file in enumerate(uploaded_files):
                st.write(f"**File Name:** {file.name}")
                ext = file.name.split('.')[-1].lower()
                
                if ext == 'txt':
                    preview = file.read(1000).decode('utf-8', errors='ignore')
                    file.seek(0)
                    st.text_area(
                        f"Preview of {file.name}",
                        preview + ("..." if len(preview) >= 1000 else ""),
                        height=150
                    )
                elif ext == 'csv':
                    try:
                        df = pd.read_csv(file)
                        file.seek(0)
                        st.write("CSV Preview (up to 5 rows)")
                        st.dataframe(df.head(5))
                    except Exception as e:
                        st.error(f"CSV preview failed: {e}")
                elif ext == 'pdf':
                    try:
                        file_bytes = file.read()
                        file.seek(0)
                        
                        pdf_file = io.BytesIO(file_bytes)
                        reader = PyPDF2.PdfReader(pdf_file, strict=False)
                        
                        pc = len(reader.pages)
                        st.write(f"PDF File: {pc} pages")
                        
                        if pc > 0:
                            try:
                                page_text = reader.pages[0].extract_text()
                                preview = page_text[:500] if page_text else "(No text extracted)"
                                st.text_area("Preview of the first page", preview + "...", height=150)
                            except:
                                st.warning("Failed to extract text from the first page")
                    except Exception as e:
                        st.error(f"PDF preview failed: {e}")

                if idx < file_count - 1:
                    st.divider()

    # Display existing messages
    for m in st.session_state.messages:
        with st.chat_message(m["role"]):
            # Process markdown to allow clickable links and properly rendered content
            st.markdown(m["content"], unsafe_allow_html=True)
            
            # Display images if present
            if "images" in m and m["images"]:
                st.subheader("Related Images")
                cols = st.columns(min(3, len(m["images"])))
                for i, (img_url, caption) in enumerate(m["images"]):
                    col_idx = i % len(cols)
                    with cols[col_idx]:
                        st.image(img_url, caption=caption, use_column_width=True)
            
            # Display videos if present
            if "videos" in m and m["videos"]:
                st.subheader("Related Videos")
                for video in m["videos"]:
                    st.markdown(f"[🎬 {video['title']}]({video['url']})", unsafe_allow_html=True)

    # User input
    query = st.chat_input("Enter your query or question here.")
    if query:
        process_input(query, uploaded_files)

    # 사이드바 하단 배지(링크) 추가
    sb.markdown("---")
    sb.markdown("Created by [https://ginigen.com](https://ginigen.com) | [YouTube Channel](https://www.youtube.com/@ginipickaistudio)")

def process_example(topic):
    """Process the selected example query."""
    process_input(topic, [])

def process_input(query: str, uploaded_files):
    # Add user's message
    if not any(m["role"] == "user" and m["content"] == query for m in st.session_state.messages):
        st.session_state.messages.append({"role": "user", "content": query})

    with st.chat_message("user"):
        st.markdown(query)
    
    with st.chat_message("assistant"):
        placeholder = st.empty()
        message_placeholder = st.empty()
        full_response = ""

        use_web_search = st.session_state.web_search_enabled
        has_uploaded_files = bool(uploaded_files) and len(uploaded_files) > 0
        
        try:
            # 상태 표시를 위한 상태 컴포넌트
            status = st.status("Preparing to answer your query...")
            status.update(label="Initializing client...")
            
            client = get_openai_client()
            
            # Prepare conversation messages
            messages = []
            
            # Web search
            search_content = None
            if use_web_search:
                status.update(label="Performing web search...")
                with st.spinner("Searching the web..."):
                    search_content = do_web_search(keywords(query, top=5))
            
            # Process uploaded files → content
            file_content = None
            if has_uploaded_files:
                status.update(label="Processing uploaded files...")
                with st.spinner("Analyzing files..."):
                    file_content = process_uploaded_files(uploaded_files)
            
            # Get relevant images and videos (before generating response)
            status.update(label="Finding related media...")
            related_images = get_images_for_query(query) if use_web_search else []
            related_videos = get_videos_for_query(query) if use_web_search else []
            
            # Build system prompt
            status.update(label="Preparing comprehensive answer...")
            sys_prompt = get_system_prompt(
                mode=st.session_state.search_mode,
                style=st.session_state.response_style,
                include_search_results=use_web_search,
                include_uploaded_files=has_uploaded_files
            )

            # OpenAI API 호출 준비
            status.update(label="Generating response...")
            
            # 메시지 구성
            api_messages = [
                {"role": "system", "content": sys_prompt}
            ]
            
            user_content = query
            
            # 검색 결과가 있으면 사용자 프롬프트에 추가
            if search_content:
                user_content += "\n\n" + search_content
            
            # 파일 내용이 있으면 사용자 프롬프트에 추가
            if file_content:
                user_content += "\n\n" + file_content
            
            # Add image and video information to the prompt
            if related_images:
                user_content += "\n\n# Related Images\n"
                for i, img_url in enumerate(related_images):
                    user_content += f"\n![Image {i+1}]({img_url})"
            
            if related_videos:
                user_content += "\n\n# Related Videos\n"
                for video in related_videos:
                    user_content += f"\n[Video: {video['title']}]({video['url']})"
            
            # 사용자 메시지 추가
            api_messages.append({"role": "user", "content": user_content})
            
            # OpenAI API 스트리밍 호출 - 고정 모델 "gpt-4.1-mini" 사용
            try:
                # 스트리밍 방식으로 API 호출
                stream = client.chat.completions.create(
                    model="gpt-4.1-mini",  # 고정 모델 사용
                    messages=api_messages,
                    temperature=1,
                    max_tokens=MAX_TOKENS,
                    top_p=1,
                    stream=True  # 스트리밍 활성화
                )
                
                # 스트리밍 응답 처리
                for chunk in stream:
                    if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].delta.content is not None:
                        content_delta = chunk.choices[0].delta.content
                        full_response += content_delta
                        message_placeholder.markdown(full_response + "▌", unsafe_allow_html=True)
                
                # 최종 응답 표시 (커서 제거)
                message_placeholder.markdown(full_response, unsafe_allow_html=True)
                
                # Display related images if available
                if related_images:
                    image_captions = [f"Related image {i+1}" for i in range(len(related_images))]
                    images_with_captions = list(zip(related_images, image_captions))
                    
                    cols = st.columns(min(3, len(related_images)))
                    for i, (img_url, caption) in enumerate(images_with_captions):
                        col_idx = i % len(cols)
                        with cols[col_idx]:
                            st.image(img_url, caption=caption, use_column_width=True)
                
                # Display related videos if available
                if related_videos:
                    st.subheader("Related Videos")
                    for video in related_videos:
                        st.markdown(f"[🎬 {video['title']}]({video['url']})", unsafe_allow_html=True)
                
                status.update(label="Response completed!", state="complete")
                
                # Save the response with images and videos in the session state
                st.session_state.messages.append({
                    "role": "assistant", 
                    "content": full_response,
                    "images": list(zip(related_images, image_captions)) if related_images else [],
                    "videos": related_videos
                })
                
            except Exception as api_error:
                error_message = str(api_error)
                logging.error(f"API error: {error_message}")
                status.update(label=f"Error: {error_message}", state="error")
                raise Exception(f"Response generation error: {error_message}")
            
            # Additional image generation if enabled
            if st.session_state.generate_image and full_response:
                with st.spinner("Generating custom image..."):
                    try:
                        ip = extract_image_prompt(full_response, query)
                        img, cap = generate_image(ip)
                        if img:
                            st.subheader("AI-Generated Image")
                            st.image(img, caption=cap)
                    except Exception as img_error:
                        logging.error(f"Image generation error: {str(img_error)}")
                        st.warning("Custom image generation failed. Using web images only.")

            # Download buttons
            if full_response:
                st.subheader("Download This Response")
                c1, c2 = st.columns(2)
                c1.download_button(
                    "Markdown", 
                    data=full_response, 
                    file_name=f"{query[:30]}.md",
                    mime="text/markdown"
                )
                c2.download_button(
                    "HTML",
                    data=md_to_html(full_response, query[:30]),
                    file_name=f"{query[:30]}.html",
                    mime="text/html"
                )

            # Auto save
            if st.session_state.auto_save and st.session_state.messages:
                try:
                    fn = f"conversation_history_auto_{datetime.now():%Y%m%d_%H%M%S}.json"
                    with open(fn, "w", encoding="utf-8") as fp:
                        json.dump(st.session_state.messages, fp, ensure_ascii=False, indent=2)
                except Exception as e:
                    logging.error(f"Auto-save failed: {e}")

        except Exception as e:
            error_message = str(e)
            placeholder.error(f"An error occurred: {error_message}")
            logging.error(f"Process input error: {error_message}")
            ans = f"An error occurred while processing your request: {error_message}"
            st.session_state.messages.append({"role": "assistant", "content": ans})


# ──────────────────────────────── main ────────────────────────────────────
def main():
    perplexity_app()

if __name__ == "__main__":
    main()