app.py

import os
import json
import time
import gradio as gr
from datetime import datetime
from typing import List, Dict, Any, Optional, Union
import threading
import re

# Import Groq
from groq import Groq

class CreativeAgenticAI:
    """
    Creative Agentic AI Chat Tool using Groq's models with browser search and compound models
    """
    
    def __init__(self, api_key: str, model: str = "compound-beta"):
        """
        Initialize the Creative Agentic AI system.
        
        Args:
            api_key: Groq API key
            model: Which Groq model to use
        """
        self.api_key = api_key
        if not self.api_key:
            raise ValueError("No API key provided")
        
        self.client = Groq(api_key=self.api_key)
        self.model = model
        self.conversation_history = []
        
        # Available models with their capabilities
        self.available_models = {
            "compound-beta": {"supports_web_search": True, "supports_browser_search": False},
            "compound-beta-mini": {"supports_web_search": True, "supports_browser_search": False},
            "openai/gpt-oss-20b": {"supports_web_search": False, "supports_browser_search": True},
            "llama-3.3-70b-versatile": {"supports_web_search": False, "supports_browser_search": True},
            "llama-3.1-70b-versatile": {"supports_web_search": False, "supports_browser_search": True},
            "mixtral-8x7b-32768": {"supports_web_search": False, "supports_browser_search": True},
        }
        
    def chat(self, message: str, 
             include_domains: List[str] = None,
             exclude_domains: List[str] = None,
             system_prompt: str = None,
             temperature: float = 0.7,
             max_tokens: int = 1024,
             search_type: str = "auto",
             force_search: bool = False) -> Dict:
        """
        Send a message to the AI and get a response with flexible search options
        
        Args:
            message: User's message
            include_domains: List of domains to include for web search
            exclude_domains: List of domains to exclude from web search
            system_prompt: Custom system prompt
            temperature: Model temperature (0.0-2.0)
            max_tokens: Maximum tokens in response
            search_type: 'web_search', 'browser_search', 'auto', or 'none'
            force_search: Force the AI to use search tools
            
        Returns:
            AI response with metadata
        """
        # Enhanced system prompt for better citation behavior
        if not system_prompt:
            citation_instruction = """
IMPORTANT: When you search the web and find information, you MUST:
1. Always cite your sources with clickable links in this format: [Source Title](URL)
2. Include multiple diverse sources when possible
3. Show which specific websites you used for each claim
4. At the end of your response, provide a "Sources Used" section with all the links
5. Be transparent about which information comes from which source
"""
            
            domain_context = ""
            if include_domains and self._supports_web_search():
                domain_context = f"\nYou are restricted to searching ONLY these domains: {', '.join(include_domains)}. Make sure to find and cite sources specifically from these domains."
            elif exclude_domains and self._supports_web_search():
                domain_context = f"\nAvoid searching these domains: {', '.join(exclude_domains)}. Search everywhere else on the web."
            
            search_instruction = ""
            if search_type == "browser_search":
                search_instruction = "\nUse browser search tools to find the most current and relevant information from the web."
            elif search_type == "web_search":
                search_instruction = "\nUse web search capabilities to find relevant information."
            elif force_search:
                if self._supports_browser_search():
                    search_instruction = "\nYou MUST use search tools to find current information before responding."
                elif self._supports_web_search():
                    search_instruction = "\nYou MUST use web search to find current information before responding."
            
            system_prompt = f"""You are a creative and intelligent AI assistant with agentic capabilities. 
            You can search the web, analyze information, and provide comprehensive responses. 
            Be helpful, creative, and engaging while maintaining accuracy.
            
            {citation_instruction}
            {domain_context}
            {search_instruction}
            
            Your responses should be well-structured, informative, and properly cited with working links."""
        
        # Build messages
        messages = [{"role": "system", "content": system_prompt}]
        
        # Add conversation history (last 10 exchanges)
        messages.extend(self.conversation_history[-20:])  # Last 10 user-assistant pairs
        
        # Add current message with context
        enhanced_message = message
        if include_domains or exclude_domains:
            filter_context = []
            if include_domains:
                filter_context.append(f"ONLY search these domains: {', '.join(include_domains)}")
            if exclude_domains:
                filter_context.append(f"EXCLUDE these domains: {', '.join(exclude_domains)}")
            enhanced_message += f"\n\n[Domain Filtering: {' | '.join(filter_context)}]"
        
        messages.append({"role": "user", "content": enhanced_message})
        
        # Set up API parameters
        params = {
            "messages": messages,
            "model": self.model,
            "temperature": temperature,
            "max_completion_tokens": max_tokens if self._supports_browser_search() else None,
            "max_tokens": max_tokens if not self._supports_browser_search() else None,
        }
        
        # Add domain filtering for compound models
        if self._supports_web_search():
            if include_domains and include_domains[0].strip():
                params["include_domains"] = [domain.strip() for domain in include_domains if domain.strip()]
            if exclude_domains and exclude_domains[0].strip():
                params["exclude_domains"] = [domain.strip() for domain in exclude_domains if domain.strip()]
        
        # Add tools based on search type and model capabilities
        tools = []
        tool_choice = None
        
        if search_type == "browser_search" and self._supports_browser_search():
            tools = [{"type": "browser_search"}]
            tool_choice = "required" if force_search else "auto"
        elif search_type == "auto":
            if self._supports_browser_search():
                tools = [{"type": "browser_search"}]
                tool_choice = "required" if force_search else "auto"
        elif force_search and self._supports_browser_search():
            tools = [{"type": "browser_search"}]
            tool_choice = "required"
        
        if tools:
            params["tools"] = tools
            params["tool_choice"] = tool_choice
        
        try:
            # Make the API call
            response = self.client.chat.completions.create(**params)
            content = response.choices[0].message.content
            
            # Extract tool usage information and enhance it
            tool_info = self._extract_tool_info(response)
            
            # Process content to enhance citations
            processed_content = self._enhance_citations(content, tool_info)
            
            # Add to conversation history
            self.conversation_history.append({"role": "user", "content": message})
            self.conversation_history.append({"role": "assistant", "content": processed_content})
            
            # Create response object
            response_data = {
                "content": processed_content,
                "timestamp": datetime.now().isoformat(),
                "model": self.model,
                "tool_usage": tool_info,
                "search_type_used": search_type,
                "parameters": {
                    "temperature": temperature,
                    "max_tokens": max_tokens,
                    "include_domains": include_domains,
                    "exclude_domains": exclude_domains,
                    "force_search": force_search
                }
            }
            
            return response_data
            
        except Exception as e:
            error_msg = f"Error: {str(e)}"
            self.conversation_history.append({"role": "user", "content": message})
            self.conversation_history.append({"role": "assistant", "content": error_msg})
            
            return {
                "content": error_msg,
                "timestamp": datetime.now().isoformat(),
                "model": self.model,
                "tool_usage": None,
                "error": str(e)
            }
    
    def _supports_web_search(self) -> bool:
        """Check if current model supports web search (compound models)"""
        return self.available_models.get(self.model, {}).get("supports_web_search", False)
    
    def _supports_browser_search(self) -> bool:
        """Check if current model supports browser search tools"""
        return self.available_models.get(self.model, {}).get("supports_browser_search", False)
    
    def _extract_tool_info(self, response) -> Dict:
        """Extract tool usage information in a JSON serializable format"""
        tool_info = {
            "tools_used": [],
            "search_queries": [],
            "sources_found": []
        }
        
        # Check for executed_tools attribute (compound models)
        if hasattr(response.choices[0].message, 'executed_tools'):
            tools = response.choices[0].message.executed_tools
            if tools:
                for tool in tools:
                    tool_dict = {
                        "tool_type": getattr(tool, "type", "unknown"),
                        "tool_name": getattr(tool, "name", "unknown"),
                    }
                    
                    # Extract search queries and results
                    if hasattr(tool, "input"):
                        tool_input = str(tool.input)
                        tool_dict["input"] = tool_input
                        # Try to extract search query
                        if "search" in tool_dict["tool_name"].lower():
                            tool_info["search_queries"].append(tool_input)
                    
                    if hasattr(tool, "output"):
                        tool_output = str(tool.output)
                        tool_dict["output"] = tool_output
                        # Try to extract URLs from output
                        urls = self._extract_urls(tool_output)
                        tool_info["sources_found"].extend(urls)
                    
                    tool_info["tools_used"].append(tool_dict)
        
        # Check for tool_calls attribute (browser search models)
        if hasattr(response.choices[0].message, 'tool_calls') and response.choices[0].message.tool_calls:
            for tool_call in response.choices[0].message.tool_calls:
                tool_dict = {
                    "tool_type": tool_call.type if hasattr(tool_call, 'type') else "browser_search",
                    "tool_name": tool_call.function.name if hasattr(tool_call, 'function') else "browser_search",
                    "tool_id": tool_call.id if hasattr(tool_call, 'id') else None
                }
                
                if hasattr(tool_call, 'function') and hasattr(tool_call.function, 'arguments'):
                    try:
                        args = json.loads(tool_call.function.arguments) if isinstance(tool_call.function.arguments, str) else tool_call.function.arguments
                        tool_dict["arguments"] = args
                        if "query" in args:
                            tool_info["search_queries"].append(args["query"])
                    except:
                        tool_dict["arguments"] = str(tool_call.function.arguments)
                
                tool_info["tools_used"].append(tool_dict)
        
        return tool_info
    
    def _extract_urls(self, text: str) -> List[str]:
        """Extract URLs from text"""
        url_pattern = r'https?://[^\s<>"]{2,}'
        urls = re.findall(url_pattern, text)
        return list(set(urls))  # Remove duplicates
    
    def _enhance_citations(self, content: str, tool_info: Dict) -> str:
        """Enhance content with better citation formatting"""
        if not tool_info or not tool_info.get("sources_found"):
            return content
        
        # Add sources section if not already present
        if "Sources Used:" not in content and "sources:" not in content.lower():
            sources_section = "\n\n---\n\n### 📚 Sources Used:\n"
            for i, url in enumerate(tool_info["sources_found"][:10], 1):  # Limit to 10 sources
                # Try to extract domain name for better formatting
                domain = self._extract_domain(url)
                sources_section += f"{i}. [{domain}]({url})\n"
            
            content += sources_section
        
        return content
    
    def _extract_domain(self, url: str) -> str:
        """Extract domain name from URL for display"""
        try:
            if url.startswith(('http://', 'https://')):
                domain = url.split('/')[2]
                # Remove www. prefix if present
                if domain.startswith('www.'):
                    domain = domain[4:]
                return domain
            return url
        except:
            return url
    
    def get_model_info(self) -> Dict:
        """Get information about current model capabilities"""
        return self.available_models.get(self.model, {})
    
    def clear_history(self):
        """Clear conversation history"""
        self.conversation_history = []
    
    def get_history_summary(self) -> str:
        """Get a summary of conversation history"""
        if not self.conversation_history:
            return "No conversation history"
        
        user_messages = [msg for msg in self.conversation_history if msg["role"] == "user"]
        assistant_messages = [msg for msg in self.conversation_history if msg["role"] == "assistant"]
        
        return f"Conversation: {len(user_messages)} user messages, {len(assistant_messages)} assistant responses"

# Global variables
ai_instance = None
api_key_status = "Not Set"

def validate_api_key(api_key: str, model: str) -> str:
    """Validate Groq API key and initialize AI instance"""
    global ai_instance, api_key_status
    
    if not api_key or len(api_key.strip()) < 10:
        api_key_status = "Invalid ❌"
        return "❌ Please enter a valid API key (should be longer than 10 characters)"
    
    try:
        # Test the API key
        client = Groq(api_key=api_key)
        # Try a simple request to validate
        test_response = client.chat.completions.create(
            messages=[{"role": "user", "content": "Hello"}],
            model=model,
            max_completion_tokens=10 if model in ["openai/gpt-oss-20b", "llama-3.3-70b-versatile", "llama-3.1-70b-versatile", "mixtral-8x7b-32768"] else None,
            max_tokens=10 if model in ["compound-beta", "compound-beta-mini"] else None
        )
        
        # Create AI instance
        ai_instance = CreativeAgenticAI(api_key=api_key, model=model)
        api_key_status = "Valid ✅"
        
        model_info = ai_instance.get_model_info()
        capabilities = []
        if model_info.get("supports_web_search"):
            capabilities.append("🌐 Web Search with Domain Filtering")
        if model_info.get("supports_browser_search"):
            capabilities.append("🔍 Browser Search Tools")
        
        cap_text = " | ".join(capabilities) if capabilities else "💬 Chat Only"
        
        return f"✅ API Key Valid! NeuroScope AI is ready.\n\n**Model:** {model}\n**Capabilities:** {cap_text}\n**Status:** Connected and ready for chat!"
        
    except Exception as e:
        api_key_status = "Invalid ❌"
        ai_instance = None
        return f"❌ Error validating API key: {str(e)}\n\nPlease check your API key and try again."

def update_model(model: str) -> str:
    """Update the model selection"""
    global ai_instance
    
    if ai_instance:
        ai_instance.model = model
        model_info = ai_instance.get_model_info()
        capabilities = []
        if model_info.get("supports_web_search"):
            capabilities.append("🌐 Web Search with Domain Filtering")
        if model_info.get("supports_browser_search"):
            capabilities.append("🔍 Browser Search Tools")
        
        cap_text = " | ".join(capabilities) if capabilities else "💬 Chat Only"
        return f"✅ Model updated to: **{model}**\n**Capabilities:** {cap_text}"
    else:
        return "⚠️ Please set your API key first"

def get_search_options(model: str) -> gr.update:
    """Get available search options based on model"""
    if not ai_instance:
        return gr.update(choices=["none"], value="none")
    
    model_info = ai_instance.available_models.get(model, {})
    options = ["none"]
    
    if model_info.get("supports_web_search"):
        options.extend(["web_search", "auto"])
    if model_info.get("supports_browser_search"):
        options.extend(["browser_search", "auto"])
    
    # Remove duplicates while preserving order
    options = list(dict.fromkeys(options))
    
    default_value = "auto" if "auto" in options else "none"
    return gr.update(choices=options, value=default_value)

def chat_with_ai(message: str, 
                include_domains: str, 
                exclude_domains: str,
                system_prompt: str,
                temperature: float,
                max_tokens: int,
                search_type: str,
                force_search: bool,
                history: List) -> tuple:
    """Main chat function"""
    global ai_instance
    
    if not ai_instance:
        error_msg = "⚠️ Please set your Groq API key first!"
        history.append([message, error_msg])
        return history, ""
    
    if not message.strip():
        return history, ""
    
    # Process domain lists
    include_list = [d.strip() for d in include_domains.split(",")] if include_domains.strip() else []
    exclude_list = [d.strip() for d in exclude_domains.split(",")] if exclude_domains.strip() else []
    
    try:
        # Get AI response
        response = ai_instance.chat(
            message=message,
            include_domains=include_list if include_list else None,
            exclude_domains=exclude_list if exclude_list else None,
            system_prompt=system_prompt if system_prompt.strip() else None,
            temperature=temperature,
            max_tokens=int(max_tokens),
            search_type=search_type,
            force_search=force_search
        )
        
        # Format response
        ai_response = response["content"]
        
        # Add enhanced tool usage info
        if response.get("tool_usage"):
            tool_info = response["tool_usage"]
            tool_summary = []
            
            if tool_info.get("search_queries"):
                tool_summary.append(f"🔍 Search queries: {len(tool_info['search_queries'])}")
            
            if tool_info.get("sources_found"):
                tool_summary.append(f"📄 Sources found: {len(tool_info['sources_found'])}")
            
            if tool_info.get("tools_used"):
                tool_types = [tool.get("tool_type", "unknown") for tool in tool_info["tools_used"]]
                unique_types = list(set(tool_types))
                tool_summary.append(f"🔧 Tools used: {', '.join(unique_types)}")
            
            if tool_summary:
                ai_response += f"\n\n*{' | '.join(tool_summary)}*"
        
        # Add search type info
        search_info = []
        if response.get("search_type_used") and response["search_type_used"] != "none":
            search_info.append(f"🔍 Search type: {response['search_type_used']}")
        
        if force_search:
            search_info.append("⚡ Forced search enabled")
        
        # Add domain filtering info
        if include_list or exclude_list:
            filter_info = []
            if include_list:
                filter_info.append(f"✅ Included domains: {', '.join(include_list)}")
            if exclude_list:
                filter_info.append(f"❌ Excluded domains: {', '.join(exclude_list)}")
            search_info.extend(filter_info)
        
        if search_info:
            ai_response += f"\n\n*🌐 Search settings: {' | '.join(search_info)}*"
        
        # Add to history
        history.append([message, ai_response])
        
        return history, ""
        
    except Exception as e:
        error_msg = f"❌ Error: {str(e)}"
        history.append([message, error_msg])
        return history, ""

def clear_chat_history():
    """Clear the chat history"""
    global ai_instance
    if ai_instance:
        ai_instance.clear_history()
    return []

def create_gradio_app():
    """Create the main Gradio application"""
    
    # Custom CSS for better styling
    css = """
    .container {
        max-width: 1200px;
        margin: 0 auto;
    }
    .header {
        text-align: center;
        background: linear-gradient(to right, #00ff94, #00b4db);
        color: white;
        padding: 20px;
        border-radius: 10px;
        margin-bottom: 20px;
    }
    .status-box {
        background-color: #f8f9fa;
        border: 1px solid #dee2e6;
        border-radius: 8px;
        padding: 15px;
        margin: 10px 0;
    }
    .example-box {
        background-color: #e8f4fd;
        border-left: 4px solid #007bff;
        padding: 15px;
        margin: 10px 0;
        border-radius: 0 8px 8px 0;
    }
    .domain-info {
        background-color: #fff3cd;
        border: 1px solid #ffeaa7;
        border-radius: 8px;
        padding: 15px;
        margin: 10px 0;
    }
    .citation-info {
        background-color: #d1ecf1;
        border: 1px solid #bee5eb;
        border-radius: 8px;
        padding: 15px;
        margin: 10px 0;
    }
    .search-info {
        background-color: #e2e3e5;
        border: 1px solid #c6c8ca;
        border-radius: 8px;
        padding: 15px;
        margin: 10px 0;
    }
    #neuroscope-accordion {
        background: linear-gradient(to right, #00ff94, #00b4db); 
        border-radius: 8px;
    }
    """
    
    with gr.Blocks(css=css, title="🤖 Creative Agentic AI Chat", theme=gr.themes.Ocean()) as app:
        
        # Header
        gr.HTML("""
        <div class="header">
            <h1>🤖 NeuroScope-AI Enhanced</h1>
            <p>Powered by Groq's Models with Web Search, Browser Search & Agentic Capabilities</p>
        </div>
        """)

        # NeuroScope AI Section
        with gr.Group():
            with gr.Accordion("🤖 NeuroScope AI Enhanced", open=False, elem_id="neuroscope-accordion"):
                gr.Markdown("""
                    **Enhanced with Multiple Search Capabilities:**
                    - 🧠 **Intelligence** (Neuro): Advanced AI reasoning across multiple models
                    - 🔍 **Precision Search** (Scope): Domain filtering + Browser search tools
                    - 🤖 **AI Capabilities** (AI): Agentic behavior with tool usage
                    - ⚡ **Dual Search**: Web search (compound models) + Browser search (other models)
                    - 🎯 **Model Flexibility**: Choose the right model for your task
                """)
                
        # IMPORTANT Section with Enhanced Search Info
        with gr.Group():
            with gr.Accordion("🔍 IMPORTANT - Enhanced Search Capabilities!", open=True, elem_id="neuroscope-accordion"):
                gr.Markdown("""
                <div class="search-info">
                <h3>🚀 NEW: Multiple Search Types Available!</h3>
                
                <h4>🌐 Web Search Models (Compound Models)</h4>
                <ul>
                    <li><strong>compound-beta:</strong> Most powerful with domain filtering</li>
                    <li><strong>compound-beta-mini:</strong> Faster with domain filtering</li>
                    <li><strong>Features:</strong> Include/exclude domains, autonomous web search</li>
                </ul>
                
                <h4>🔍 Browser Search Models (Tool-based Models)</h4>
                <ul>
                    <li><strong>openai/gpt-oss-20b:</strong> Fast browser search capabilities</li>
                    <li><strong>llama-3.3-70b-versatile:</strong> Advanced reasoning with search</li>
                    <li><strong>llama-3.1-70b-versatile:</strong> Reliable with search tools</li>
                    <li><strong>mixtral-8x7b-32768:</strong> Large context with search</li>
                    <li><strong>Features:</strong> Real-time browser search, current information</li>
                </ul>
                </div>
                
                <div class="citation-info">
                <h3>🔗 Enhanced Citation System</h3>
                <p>All models now include:</p>
                <ul>
                    <li><strong>Automatic Source Citations:</strong> Clickable links to sources</li>
                    <li><strong>Sources Used Section:</strong> Dedicated section showing all websites</li>
                    <li><strong>Search Type Indication:</strong> Shows which search method was used</li>
                    <li><strong>Tool Usage Display:</strong> Transparent about AI's research process</li>
                </ul>
                </div>
                """)
        
        # API Key and Model Selection Section
        with gr.Row():
            with gr.Column(scale=2):
                api_key = gr.Textbox(
                    label="🔑 Groq API Key",
                    placeholder="Enter your Groq API key here...",
                    type="password",
                    info="Get your API key from: https://console.groq.com/"
                )
                
        # Advanced Settings
        with gr.Accordion("⚙️ Advanced Settings", open=False, elem_id="neuroscope-accordion"):
            with gr.Row():
                temperature = gr.Slider(
                    minimum=0.0,
                    maximum=2.0,
                    value=0.7,
                    step=0.1,
                    label="🌡️ Temperature",
                    info="Higher = more creative, Lower = more focused"
                )
                max_tokens = gr.Slider(
                    minimum=100,
                    maximum=4000,
                    value=1024,
                    step=100,
                    label="📝 Max Tokens",
                    info="Maximum length of response"
                )
                
            system_prompt = gr.Textbox(
                label="🎭 Custom System Prompt",
                placeholder="Override the default system prompt...",
                lines=3,
                info="Leave empty to use default creative assistant prompt with enhanced citations"
            )
        
        # Model Comparison Section
        with gr.Accordion("📊 Model Comparison Guide", open=False, elem_id="neuroscope-accordion"):
            gr.Markdown("""
            ### 🔍 Choose Your Model Based on Task:
            
            **For Academic Research & Domain-Specific Search:**
            - `compound-beta` or `compound-beta-mini` with include domains (*.edu, arxiv.org)
            - Best for: Research papers, academic sources, filtered searches
            
            **For Current Events & Real-Time Information:**
            - `openai/gpt-oss-20b` or `llama-3.3-70b-versatile` with browser search
            - Best for: News, current events, real-time data
            
            **For General Knowledge & Creative Tasks:**
            - Any model with search type = "auto" or "none"
            - Best for: Creative writing, general questions, analysis
            
            **For Programming & Technical Documentation:**
            - `llama-3.1-70b-versatile` with browser search, or compound models with tech domains
            - Best for: Code help, documentation, technical guides
            """)
        
        # Domain Examples Section
        with gr.Accordion("🔗 Common Domain Examples", open=False, elem_id="neuroscope-accordion"):
            gr.Markdown("""
            **Academic & Research:**
            - `arxiv.org`, `*.edu`, `scholar.google.com`, `researchgate.net`, `pubmed.ncbi.nlm.nih.gov`
            
            **Technology & Programming:**
            - `github.com`, `stackoverflow.com`, `docs.python.org`, `developer.mozilla.org`, `medium.com`
            
            **News & Media:**
            - `reuters.com`, `bbc.com`, `npr.org`, `apnews.com`, `cnn.com`, `nytimes.com`
            
            **Business & Finance:**
            - `bloomberg.com`, `wsj.com`, `nasdaq.com`, `sec.gov`, `investopedia.com`
            
            **Science & Medicine:**
            - `nature.com`, `science.org`, `pubmed.ncbi.nlm.nih.gov`, `who.int`, `cdc.gov`
            
            **Government & Official:**
            - `*.gov`, `*.org`, `un.org`, `worldbank.org`, `imf.org`
            """)
        
        # How to Use Section
        with gr.Accordion("📖 How to Use This Enhanced App", open=False, elem_id="neuroscope-accordion"):
            gr.Markdown("""
            ### 🚀 Getting Started
            1. **Enter your Groq API Key** - Get one from [console.groq.com](https://console.groq.com/)
            2. **Select a model** - Choose based on your search needs:
               - **Compound models**: For web search with domain filtering
               - **Tool-based models**: For browser search with real-time data
            3. **Configure search settings** - Choose search type and options
            4. **Click Connect** - Validate your key and connect to the AI
            5. **Start chatting!** - Type your message and get intelligent responses with citations
            
            ### 🎯 Key Features
            - **Dual Search Capabilities**: Web search + Browser search depending on model
            - **Smart Citations**: Automatic source linking and citation formatting
            - **Domain Filtering**: Control which websites the AI searches (compound models)
            - **Real-time Search**: Get current information with browser search tools
            - **Model Flexibility**: Choose the right model for your specific task
            - **Enhanced Tool Visibility**: See exactly what search tools were used
            
            ### 💡 Tips for Best Results
            
            **For Research Tasks:**
            - Use compound models with domain filtering
            - Include academic domains (*.edu, arxiv.org) for scholarly sources
            - Use "Force Search" for the most current information
            
            **For Current Events:**
            - Use tool-based models (openai/gpt-oss-20b, llama models)
            - Set search type to "browser_search"
            - Enable "Force Search" for real-time data
            
            **For Creative Tasks:**
            - Any model works well
            - Set search type to "none" for purely creative responses
            - Use higher temperature (0.8-1.0) for more creativity
            
            **For Technical Questions:**
            - Use llama-3.1-70b-versatile for programming
            - Include tech domains (github.com, stackoverflow.com) with compound models
            - Use browser search for latest documentation
            """)
        
        # Sample Examples Section
        with gr.Accordion("🎯 Sample Examples to Test Enhanced Search", open=False, elem_id="neuroscope-accordion"):
            gr.Markdown("""
            <div class="example-box">
            <h4>🔬 Research & Analysis (Test Different Models)</h4>
            
            **Compound Model + Domain Filtering:**
            - Query: "What are the latest breakthroughs in quantum computing?"
            - Model: compound-beta
            - Include domains: "arxiv.org, *.edu, nature.com"
            - Search type: web_search
            
            **Browser Search Model:**
            - Same query with openai/gpt-oss-20b
            - Search type: browser_search
            - Force search: enabled
            
            <h4>📰 Current Events (Browser Search Excellence)</h4>
            
            **Real-time News:**
            - Query: "What happened in AI industry this week?"
            - Model: llama-3.3-70b-versatile
            - Search type: browser_search
            - Force search: enabled
            
            **Compare with Web Search:**
            - Same query with compound-beta
            - Include domains: "reuters.com, bbc.com, techcrunch.com"
            
            <h4>💻 Programming & Tech (Model Comparison)</h4>
            
            **Technical Documentation:**
            - Query: "How to implement OAuth 2.0 in Python Flask?"
            - Try with both model types:
              - compound-beta with "github.com, docs.python.org, stackoverflow.com"
              - llama-3.1-70b-versatile with browser_search
            
            <h4>🎨 Creative Tasks (No Search Needed)</h4>
            - Query: "Write a short story about AI and humans working together"
            - Any model with search_type: "none"
            - Higher temperature (0.8-1.0)
            
            <h4>📊 Business Analysis (Filtered vs Real-time)</h4>
            
            **Financial Data (Real-time):**
            - Query: "Current cryptocurrency market trends"
            - Model: openai/gpt-oss-20b
            - Search type: browser_search
            - Force search: enabled
            
            **Business Analysis (Filtered):**
            - Query: "Cryptocurrency adoption in enterprise"
            - Model: compound-beta
            - Include domains: "bloomberg.com, wsj.com, harvard.edu"
            </div>
            """)
        
        # Event handlers
        send_btn.click(
            fn=chat_with_ai,
            inputs=[msg, include_domains, exclude_domains, system_prompt, temperature, max_tokens, search_type, force_search, chatbot],
            outputs=[chatbot, msg]
        )
        
        msg.submit(
            fn=chat_with_ai,
            inputs=[msg, include_domains, exclude_domains, system_prompt, temperature, max_tokens, search_type, force_search, chatbot],
            outputs=[chatbot, msg]
        )
        
        clear_btn.click(
            fn=clear_chat_history,
            outputs=[chatbot]
        )
        
        # Footer
        with gr.Accordion("🚀 About This Enhanced NeuroScope AI", open=True, elem_id="neuroscope-accordion"):
            gr.Markdown("""
            **Enhanced Creative Agentic AI Chat Tool** with dual search capabilities:
            
            ### 🆕 **New in This Version:**
            - 🔍 **Browser Search Integration**: Real-time search with tool-based models
            - 🌐 **Dual Search System**: Web search (compound) + Browser search (tool-based)
            - 🎯 **Model Flexibility**: 6 different models for different tasks
            - ⚡ **Force Search Option**: Make AI search even for general questions  
            - 🔧 **Enhanced Tool Visibility**: See exactly what search tools were used
            - 📊 **Model Comparison Guide**: Choose the right model for your task
            
            ### 🏆 **Core Features:**
            - 🔗 **Automatic Source Citations**: Every response includes clickable links to sources
            - 📚 **Sources Used Section**: Dedicated section showing all websites referenced  
            - 🌐 **Smart Domain Filtering**: Control search scope (compound models)
            - 🔍 **Real-time Browser Search**: Current information (tool-based models)
            - 💬 **Conversational Memory**: Maintains context throughout the session
            - ⚙️ **Full Customization**: Adjust all parameters and prompts
            - 🎨 **Creative & Analytical**: Optimized for both creative and research tasks
            
            ### 🛠️ **Technical Details:**
            - **Compound Models**: compound-beta, compound-beta-mini (web search + domain filtering)
            - **Tool-based Models**: openai/gpt-oss-20b, llama models, mixtral (browser search tools)
            - **Automatic Search Type Detection**: AI chooses best search method
            - **Enhanced Error Handling**: Robust error management and user feedback
            - **Real-time Status Updates**: Live feedback on model capabilities and search settings
            """)
    
    return app

# Main execution
if __name__ == "__main__":
    app = create_gradio_app()
    app.launch(
        share=True,
        server_name="0.0.0.0",
        server_port=7860
    )
            with gr.Column(scale=2):
                model_selection = gr.Radio(
                    choices=[
                        "compound-beta",
                        "compound-beta-mini", 
                        "openai/gpt-oss-20b",
                        "llama-3.3-70b-versatile",
                        "llama-3.1-70b-versatile",
                        "mixtral-8x7b-32768"
                    ],
                    label="🧠 Model Selection",
                    value="compound-beta",
                    info="Choose based on your search needs"
                )
            with gr.Column(scale=1):
                connect_btn = gr.Button("🔗 Connect", variant="primary", size="lg")
        
        # Status display
        status_display = gr.Markdown("### 📊 Status: Not connected", elem_classes=["status-box"])
        
        # Connect button functionality
        connect_btn.click(
            fn=validate_api_key,
            inputs=[api_key, model_selection],
            outputs=[status_display]
        )
        
        model_selection.change(
            fn=update_model,
            inputs=[model_selection],
            outputs=[status_display]
        )
        
        # Main Chat Interface
        with gr.Tab("💬 Chat"):
            chatbot = gr.Chatbot(
                label="Creative AI Assistant with Enhanced Search",
                height=500,
                show_label=True,
                bubble_full_width=False,
                show_copy_button=True
            )
            
            with gr.Row():
                msg = gr.Textbox(
                    label="Your Message",
                    placeholder="Type your message here...",
                    lines=3
                )
                with gr.Column():
                    send_btn = gr.Button("📤 Send", variant="primary")
                    clear_btn = gr.Button("🗑️ Clear", variant="secondary")
            
        # Search Settings
        with gr.Accordion("🔍 Search Settings", open=False, elem_id="neuroscope-accordion"):
            with gr.Row():
                search_type = gr.Radio(
                    choices=["auto", "web_search", "browser_search", "none"],
                    label="🎯 Search Type",
                    value="auto",
                    info="Choose search method (auto = model decides)"
                )
                force_search = gr.Checkbox(
                    label="⚡ Force Search",
                    value=False,
                    info="Force AI to search even for general questions"
                )
            
            # Update search options when model changes
            model_selection.change(
                fn=get_search_options,
                inputs=[model_selection],
                outputs=[search_type]
            )
            
        # Domain Filtering Section (only for web search models)
        with gr.Accordion("🌐 Domain Filtering (Web Search Models Only)", open=False, elem_id="neuroscope-accordion"):
            gr.Markdown("""
            <div class="domain-info">
            <h4>🔍 Domain Filtering Guide</h4>
            <p><strong>Note:</strong> Domain filtering only works with compound models (compound-beta, compound-beta-mini)</p>
            <ul>
                <li><strong>Include Domains:</strong> Only search these domains (comma-separated)</li>
                <li><strong>Exclude Domains:</strong> Never search these domains (comma-separated)</li>
                <li><strong>Examples:</strong> arxiv.org, *.edu, github.com, stackoverflow.com</li>
                <li><strong>Wildcards:</strong> Use *.edu for all educational domains</li>
            </ul>
            </div>
            """)
            
            with gr.Row():
                include_domains = gr.Textbox(
                    label="✅ Include Domains (comma-separated)",
                    placeholder="arxiv.org, *.edu, github.com, stackoverflow.com",
                    info="Only search these domains (compound models only)"
                )
                exclude_domains = gr.Textbox(
                    label="❌ Exclude Domains (comma-separated)", 
                    placeholder="wikipedia.org, reddit.com, twitter.com",
                    info="Never search these domains (compound models only)"
                )