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 aiohttp
import asyncio

# Import Groq
from groq import Groq

class ChutesClient:
    """Client for interacting with Chutes API"""
    
    def __init__(self, api_key: str):
        self.api_key = api_key or ""
        self.base_url = "https://llm.chutes.ai/v1"
        
    async def chat_completions_create(self, **kwargs) -> Dict:
        """Make async request to Chutes chat completions endpoint"""
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }
        
        # Prepare the body for Chutes API
        body = {
            "model": kwargs.get("model", "openai/gpt-oss-20b"),
            "messages": kwargs.get("messages", []),
            "stream": kwargs.get("stream", False),
            "max_tokens": kwargs.get("max_tokens", 1024),
            "temperature": kwargs.get("temperature", 0.7)
        }
        
        async with aiohttp.ClientSession() as session:
            if body["stream"]:
                # Handle streaming response
                async with session.post(
                    f"{self.base_url}/chat/completions",
                    headers=headers,
                    json=body
                ) as response:
                    if response.status != 200:
                        raise Exception(f"Chutes API error: {await response.text()}")
                    
                    content = ""
                    async for line in response.content:
                        line = line.decode("utf-8").strip()
                        if line.startswith("data: "):
                            data = line[6:]
                            if data == "[DONE]":
                                break
                            try:
                                if data.strip():
                                    chunk_json = json.loads(data)
                                    if "choices" in chunk_json and len(chunk_json["choices"]) > 0:
                                        delta = chunk_json["choices"][0].get("delta", {})
                                        if "content" in delta and delta["content"]:
                                            content += str(delta["content"])
                            except json.JSONDecodeError:
                                continue
                    
                    # Return in OpenAI format for compatibility
                    return {
                        "choices": [{
                            "message": {
                                "content": content,
                                "role": "assistant"
                            }
                        }]
                    }
            else:
                # Handle non-streaming response
                async with session.post(
                    f"{self.base_url}/chat/completions",
                    headers=headers,
                    json=body
                ) as response:
                    if response.status != 200:
                        raise Exception(f"Chutes API error: {await response.text()}")
                    return await response.json()

class CreativeAgenticAI:
    """
    Creative Agentic AI Chat Tool using Groq and Chutes models with browser search and compound models
    """
    
    def __init__(self, groq_api_key: str, chutes_api_key: str, model: str = "compound-beta"):
        """
        Initialize the Creative Agentic AI system.
        
        Args:
            groq_api_key: Groq API key
            chutes_api_key: Chutes API key
            model: Which model to use
        """
        self.groq_api_key = str(groq_api_key) if groq_api_key else ""
        self.chutes_api_key = str(chutes_api_key) if chutes_api_key else ""
        if not self.groq_api_key and model != "openai/gpt-oss-20b":
            raise ValueError("No Groq API key provided")
        if not self.chutes_api_key and model == "openai/gpt-oss-20b":
            raise ValueError("No Chutes API key provided")
        
        self.model = str(model) if model else "compound-beta"
        self.groq_client = Groq(api_key=self.groq_api_key) if self.groq_api_key else None
        self.chutes_client = ChutesClient(api_key=self.chutes_api_key) if self.chutes_api_key else None
        self.conversation_history = []
        
        # Available models with their capabilities
        self.available_models = {
            "compound-beta": {"supports_web_search": True, "supports_browser_search": False, "api": "groq"},
            "compound-beta-mini": {"supports_web_search": True, "supports_browser_search": False, "api": "groq"},
            "openai/gpt-oss-20b": {"supports_web_search": False, "supports_browser_search": False, "api": "chutes"},
        }
        
    async 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
        """
        # Safe string conversion
        message = str(message) if message else ""
        system_prompt = str(system_prompt) if system_prompt else ""
        search_type = str(search_type) if search_type else "auto"
        
        # Enhanced system prompt for better behavior
        if not system_prompt:
            if self.model == "openai/gpt-oss-20b":
                # Simple, direct system prompt for Chutes model
                system_prompt = """You are a helpful, knowledgeable AI assistant. Provide direct, clear, complete and informative responses to user questions. Be concise but thorough. Do not include internal reasoning or commentary - just give the answer the user is looking for. Please also cite the source urls from where you got the informations."""
            else:
                # Enhanced system prompt for Groq models with search capabilities
                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():
                    safe_domains = [str(d) for d in include_domains if d]
                    domain_context = f"\nYou are restricted to searching ONLY these domains: {', '.join(safe_domains)}. Make sure to find and cite sources specifically from these domains."
                elif exclude_domains and self._supports_web_search():
                    safe_domains = [str(d) for d in exclude_domains if d]
                    domain_context = f"\nAvoid searching these domains: {', '.join(safe_domains)}. Search everywhere else on the web."
                
                search_instruction = ""
                if search_type == "browser_search" and self._supports_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}]
        messages.extend(self.conversation_history[-20:])
        
        # Enhanced message for domain filtering (only for Groq models)
        enhanced_message = message
        if (include_domains or exclude_domains) and self._supports_web_search():
            filter_context = []
            if include_domains:
                safe_domains = [str(d) for d in include_domains if d]
                if safe_domains:
                    filter_context.append(f"ONLY search these domains: {', '.join(safe_domains)}")
            if exclude_domains:
                safe_domains = [str(d) for d in exclude_domains if d]
                if safe_domains:
                    filter_context.append(f"EXCLUDE these domains: {', '.join(safe_domains)}")
            if filter_context:
                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_tokens": max_tokens,
        }
        
        # Add domain filtering for compound models (Groq only)
        if self._supports_web_search():
            if include_domains:
                safe_domains = [str(d).strip() for d in include_domains if d and str(d).strip()]
                if safe_domains:
                    params["include_domains"] = safe_domains
            if exclude_domains:
                safe_domains = [str(d).strip() for d in exclude_domains if d and str(d).strip()]
                if safe_domains:
                    params["exclude_domains"] = safe_domains
        
        # Add tools only for Groq models that support browser search
        tools = []
        tool_choice = None
        if self._supports_browser_search():
            if search_type in ["browser_search", "auto"] or force_search:
                tools = [{"type": "browser_search", "function": {"name": "browser_search"}}]
                tool_choice = "required" if force_search else "auto"
        
        if tools:
            params["tools"] = tools
            params["tool_choice"] = tool_choice
        
        try:
            # Make the API call based on model
            if self.available_models[self.model]["api"] == "chutes":
                # Use streaming for better response quality
                params["stream"] = True
                response = await self.chutes_client.chat_completions_create(**params)
                # Handle Chutes response
                content = ""
                if response and "choices" in response and response["choices"]:
                    message_content = response["choices"][0].get("message", {}).get("content")
                    content = str(message_content) if message_content else "No response content"
                else:
                    content = "No response received"
                tool_calls = None
            else:
                # Groq API call
                params["max_completion_tokens"] = params.pop("max_tokens", None)
                response = self.groq_client.chat.completions.create(**params)
                content = ""
                if response and response.choices and response.choices[0].message:
                    message_content = response.choices[0].message.content
                    content = str(message_content) if message_content else "No response content"
                else:
                    content = "No response received"
                tool_calls = response.choices[0].message.tool_calls if hasattr(response.choices[0].message, "tool_calls") else None
            
            # Extract tool usage information
            tool_info = self._extract_tool_info(response, tool_calls)
            
            # 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})
            
            return {
                "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
                }
            }
            
        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, tool_calls) -> Dict:
        """Extract tool usage information in a JSON serializable format"""
        tool_info = {
            "tools_used": [],
            "search_queries": [],
            "sources_found": []
        }
        
        # Handle Groq executed_tools
        if hasattr(response, 'choices') and hasattr(response.choices[0].message, 'executed_tools'):
            tools = response.choices[0].message.executed_tools
            if tools:
                for tool in tools:
                    tool_dict = {
                        "tool_type": str(getattr(tool, "type", "unknown")),
                        "tool_name": str(getattr(tool, "name", "unknown")),
                    }
                    if hasattr(tool, "input"):
                        tool_input = getattr(tool, "input")
                        tool_input_str = str(tool_input) if tool_input is not None else ""
                        tool_dict["input"] = tool_input_str
                        if "search" in tool_dict["tool_name"].lower():
                            tool_info["search_queries"].append(tool_input_str)
                    if hasattr(tool, "output"):
                        tool_output = getattr(tool, "output")
                        tool_output_str = str(tool_output) if tool_output is not None else ""
                        tool_dict["output"] = tool_output_str
                        urls = self._extract_urls(tool_output_str)
                        tool_info["sources_found"].extend(urls)
                    tool_info["tools_used"].append(tool_dict)
        
        # Handle tool_calls for both APIs
        if tool_calls:
            for tool_call in tool_calls:
                tool_dict = {
                    "tool_type": str(getattr(tool_call, "type", "browser_search")),
                    "tool_name": "browser_search",
                    "tool_id": str(getattr(tool_call, "id", "")) if getattr(tool_call, "id", None) else ""
                }
                if hasattr(tool_call, "function") and tool_call.function:
                    tool_dict["tool_name"] = str(getattr(tool_call.function, "name", "browser_search"))
                    if hasattr(tool_call.function, "arguments"):
                        try:
                            args_raw = tool_call.function.arguments
                            if isinstance(args_raw, str):
                                args = json.loads(args_raw)
                            else:
                                args = args_raw or {}
                            tool_dict["arguments"] = args
                            if "query" in args:
                                tool_info["search_queries"].append(str(args["query"]))
                        except:
                            args_str = str(args_raw) if args_raw is not None else ""
                            tool_dict["arguments"] = args_str
                tool_info["tools_used"].append(tool_dict)
        
        return tool_info
    
    def _extract_urls(self, text: str) -> List[str]:
        """Extract URLs from text"""
        if not text:
            return []
        text_str = str(text)
        url_pattern = r'https?://[^\s<>"]{2,}'
        urls = re.findall(url_pattern, text_str)
        return list(set(urls))
    
    def _enhance_citations(self, content: str, tool_info: Dict) -> str:
        """Enhance content with better citation formatting"""
        if not content:
            return ""
        content_str = str(content)
        if not tool_info or not tool_info.get("sources_found"):
            return content_str
        
        if "Sources Used:" not in content_str and "sources:" not in content_str.lower():
            sources_section = "\n\n---\n\n### Sources Used:\n"
            for i, url in enumerate(tool_info["sources_found"][:10], 1):
                domain = self._extract_domain(str(url))
                sources_section += f"{i}. [{domain}]({url})\n"
            content_str += sources_section
        
        return content_str
    
    def _extract_domain(self, url: str) -> str:
        """Extract domain name from URL for display"""
        if not url:
            return ""
        url_str = str(url)
        try:
            if url_str.startswith(('http://', 'https://')):
                domain = url_str.split('/')[2]
                if domain.startswith('www.'):
                    domain = domain[4:]
                return domain
            return url_str
        except:
            return url_str
    
    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"

async def validate_api_keys(groq_api_key: str, chutes_api_key: str, model: str) -> str:
    """Validate both Groq and Chutes API keys and initialize AI instance"""
    global ai_instance, api_key_status
    
    # Handle None values and convert to strings
    groq_api_key = str(groq_api_key) if groq_api_key else ""
    chutes_api_key = str(chutes_api_key) if chutes_api_key else ""
    model = str(model) if model else "compound-beta"
    
    if model == "openai/gpt-oss-20b" and not chutes_api_key.strip():
        api_key_status = "Invalid ❌"
        return "❌ Please enter a valid Chutes API key for the selected model"
    
    if model in ["compound-beta", "compound-beta-mini"] and not groq_api_key.strip():
        api_key_status = "Invalid ❌"
        return "❌ Please enter a valid Groq API key for the selected model"
    
    try:
        if model == "openai/gpt-oss-20b":
            chutes_client = ChutesClient(api_key=chutes_api_key)
            await chutes_client.chat_completions_create(
                messages=[{"role": "user", "content": "Hello"}],
                model=model,
                max_tokens=10
            )
        else:
            groq_client = Groq(api_key=groq_api_key)
            groq_client.chat.completions.create(
                messages=[{"role": "user", "content": "Hello"}],
                model=model,
                max_tokens=10
            )
        
        ai_instance = CreativeAgenticAI(groq_api_key=groq_api_key, chutes_api_key=chutes_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 Keys Valid! NeuroScope AI is ready.\n\n**Model:** {model}\n**Capabilities:** {cap_text}\n**API:** {model_info.get('api', 'unknown')}\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 keys and try again."

def update_model(model: str) -> str:
    """Update the model selection"""
    global ai_instance
    
    model = str(model) if model else "compound-beta"
    
    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}\n**API:** {model_info.get('api', 'unknown')}"
    else:
        return "⚠️ Please set your API keys 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 = str(model) if model else "compound-beta"
    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"])
    
    options = list(dict.fromkeys(options))
    default_value = "auto" if "auto" in options else "none"
    return gr.update(choices=options, value=default_value)

async 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 API keys first!"
        history.append([str(message) if message else "", error_msg])
        return history, ""
    
    # Convert all inputs to strings and handle None values
    message = str(message) if message else ""
    include_domains = str(include_domains) if include_domains else ""
    exclude_domains = str(exclude_domains) if exclude_domains else ""
    system_prompt = str(system_prompt) if system_prompt else ""
    search_type = str(search_type) if search_type else "auto"
    
    if not message.strip():
        return history, ""
    
    include_list = [d.strip() for d in include_domains.split(",") if d.strip()] if include_domains.strip() else []
    exclude_list = [d.strip() for d in exclude_domains.split(",") if d.strip()] if exclude_domains.strip() else []
    
    try:
        response = await 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
        )
        
        ai_response = str(response.get("content", "No response received"))
        
        # Add tool usage info for Groq models
        if response.get("tool_usage") and ai_instance.model != "openai/gpt-oss-20b":
            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 = [str(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 settings info
        search_info = []
        if response.get("search_type_used") and str(response["search_type_used"]) != "none":
            search_info.append(f"🔍 Search type: {response['search_type_used']}")
        
        if force_search:
            search_info.append("⚡ Forced search enabled")
        
        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 and ai_instance.model != "openai/gpt-oss-20b":
            ai_response += f"\n\n*🌐 Search settings: {' | '.join(search_info)}*"
        
        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"""
    
    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:
        gr.HTML("""
        <div class="header">
            <h1>🤖 NeuroScope-AI Enhanced</h1>
            <p>Powered by Groq and Chutes Models with Web Search and Agentic Capabilities</p>
        </div>
        """)

        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 (Groq models)
                    - 🤖 **AI Capabilities** (AI): Agentic behavior with tool usage
                    - ⚡ **Dual APIs**: Web search (Groq) + Streaming chat (Chutes)
                    - 🎯 **Model Flexibility**: Choose the right model for your task
                """)
                
        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 (Groq API)</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>💬 Chat Model (Chutes API)</h4>
                <ul>
                    <li><strong>openai/gpt-oss-20b:</strong> Fast conversational capabilities with streaming</li>
                    <li><strong>Features:</strong> General chat, streaming responses, no web search</li>
                </ul>
                </div>
                
                <div class="citation-info">
                <h3>🔗 Enhanced Citation System</h3>
                <p>Groq models 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>
                </ul>
                <p><strong>Chutes models:</strong> Direct conversational responses without web search</p>
                </div>
                """)
        
        with gr.Row():
            with gr.Column(scale=2):
                groq_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/"
                )
                chutes_api_key = gr.Textbox(
                    label="🔑 Chutes API Key",
                    placeholder="Enter your Chutes API key here...",
                    type="password",
                    info="Required for openai/gpt-oss-20b model"
                )
            with gr.Column(scale=2):
                model_selection = gr.Radio(
                    choices=[
                        "compound-beta",
                        "compound-beta-mini", 
                        "openai/gpt-oss-20b"
                    ],
                    label="🧠 Model Selection",
                    value="compound-beta",
                    info="Choose based on your needs"
                )
            with gr.Column(scale=1):
                connect_btn = gr.Button("🔗 Connect", variant="primary", size="lg")
        
        status_display = gr.Markdown("### 📊 Status: Not connected", elem_classes=["status-box"])
        
        connect_btn.click(
            fn=validate_api_keys,
            inputs=[groq_api_key, chutes_api_key, model_selection],
            outputs=[status_display]
        )
        
        model_selection.change(
            fn=update_model,
            inputs=[model_selection],
            outputs=[status_display]
        )
        
        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")
            
        with gr.Accordion("🔍 Search Settings", open=False, elem_id="neuroscope-accordion"):
            with gr.Row():
                search_type = gr.Radio(
                    choices=["auto", "web_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 (Groq models only)"
                )
            
            model_selection.change(
                fn=get_search_options,
                inputs=[model_selection],
                outputs=[search_type]
            )
            
        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)"
                )
        
        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"
            )
        
        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
            - API: Groq
            
            **For General Knowledge & Creative Tasks:**
            - `openai/gpt-oss-20b` for fast conversational responses
            - Best for: Creative writing, general questions
            - API: Chutes
            
            **For Programming & Technical Documentation:**
            - `compound-beta` with tech domains
            - Best for: Code help, documentation, technical guides
            - API: Groq
            """)
        
        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`
            """)
        
        with gr.Accordion("📖 How to Use This Enhanced App", open=False, elem_id="neuroscope-accordion"):
            gr.Markdown("""
            ### 🚀 Getting Started
            1. **Enter your API Keys** - Groq from [console.groq.com](https://console.groq.com/), Chutes for openai/gpt-oss-20b
            2. **Select a model** - Choose based on your needs:
               - **Compound models** (Groq): For web search with domain filtering
               - **openai/gpt-oss-20b** (Chutes): For general conversational tasks
            3. **Configure search settings** - Choose search type and options (Groq models only)
            4. **Click Connect** - Validate your keys and connect to the AI
            5. **Start chatting!** - Type your message and get intelligent responses with citations
            
            ### 🎯 Key Features
            - **Dual APIs**: Web search (Groq) + Basic chat (Chutes)
            - **Smart Citations**: Automatic source linking and citation formatting (Groq models)
            - **Domain Filtering**: Control which websites the AI searches (Groq models)
            - **Model Flexibility**: Choose the right model and API for your task
            - **Enhanced Tool Visibility**: See search tools used (Groq models)
            
            ### 💡 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 Creative Tasks:**
            - Use openai/gpt-oss-20b (Chutes) or any model
            - Set search type to "none" for purely creative responses
            - Use higher temperature (0.8-1.0) for more creativity
            """)
        
        with gr.Accordion("🎯 Sample Examples to Test Enhanced Search", open=False, elem_id="neuroscope-accordion"):
            gr.Markdown("""
            <div class="example-box">
            <h4>🔬 Research & Analysis</h4>
            
            **Compound Model + Domain Filtering (Groq):**
            - Query: "What are the latest breakthroughs in quantum computing?"
            - Model: compound-beta
            - Include domains: "arxiv.org, *.edu, nature.com"
            - Search type: web_search
            
            <h4>💬 General Knowledge (Chutes):**
            - Query: "Tell me about quantum computing"
            - Model: openai/gpt-oss-20b
            - Search type: none
            
            <h4>💻 Programming & Tech</h4>
            
            **Technical Documentation (Groq):**
            - Query: "How to implement OAuth 2.0 in Python Flask?"
            - Model: compound-beta
            - Include domains: "github.com, docs.python.org, stackoverflow.com"
            - Search type: web_search
            
            **Code Help (Chutes):**
            - Same query with openai/gpt-oss-20b
            - Search type: none
            
            <h4>🎨 Creative Tasks</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</h4>
            
            **Business Analysis (Filtered, Groq):**
            - Query: "Cryptocurrency adoption in enterprise"
            - Model: compound-beta
            - Include domains: "bloomberg.com, wsj.com, harvard.edu"
            - Search type: web_search
            </div>
            """)
        
        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]
        )
        
        with gr.Accordion("🚀 About This Enhanced NeuroScope AI", open=True, elem_id="neuroscope-accordion"):
            gr.Markdown("""
            **Enhanced Creative Agentic AI Chat Tool** with dual API support:
            
            ### 🆕 **New in This Version:**
            - 💬 **Chutes API Integration**: For openai/gpt-oss-20b model
            - 🌐 **Dual API System**: Web search (Groq) + Basic chat (Chutes)
            - 🎯 **Model Flexibility**: Multiple models across two APIs
            - ⚡ **Force Search Option**: Make AI search for Groq models  
            - 🔧 **Enhanced Tool Visibility**: See search tools used (Groq models)
            - 📊 **Model Comparison Guide**: Choose the right model and API
            
            ### 🏆 **Core Features:**
            - 🔗 **Automatic Source Citations**: Clickable links to sources (Groq models)
            - 📚 **Sources Used Section**: Dedicated section for websites (Groq models)
            - 🌐 **Smart Domain Filtering**: Control search scope (Groq 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 (Groq)**: compound-beta, compound-beta-mini (web search + domain filtering)
            - **Chat Model (Chutes)**: openai/gpt-oss-20b (basic conversational capabilities)
            - **Automatic Search Type Detection**: AI chooses best search method (Groq models)
            - **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
    )