App.py

import os
import asyncio
import gradio as gr
import logging
from huggingface_hub import InferenceClient
import cohere
import google.generativeai as genai
from anthropic import Anthropic
import openai
from typing import List, Dict, Any, Optional
from dotenv import load_dotenv

# Load environment variables from .env file if it exists
load_dotenv()

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# --- Agent Class ---
class PolyThinkAgent:
    def __init__(self, model_name: str, model_path: str, role: str = "solver", api_provider: str = None):
        self.model_name = model_name
        self.model_path = model_path
        self.role = role
        self.api_provider = api_provider
        self.clients = {}
        self.hf_token = None
        self.inference = None

    def set_clients(self, clients: Dict[str, Any]):
        """Set the API clients for this agent"""
        self.clients = clients
        if "huggingface" in clients:
            self.hf_token = clients["huggingface"]
            if self.hf_token:
                self.inference = InferenceClient(token=self.hf_token)

    async def solve_problem(self, problem: str) -> Dict[str, Any]:
        """Generate a solution to the given problem"""
        try:
            if self.api_provider == "cohere" and "cohere" in self.clients:
                response = self.clients["cohere"].chat(
                    model=self.model_path,
                    message=f"""
                    PROBLEM: {problem}
                    INSTRUCTIONS:
                    - Provide a clear, concise solution in one sentence.
                    - Include brief reasoning in one additional sentence.
                    - Do not repeat the solution or add extraneous text.
                    """
                )
                solution = response.text.strip()
                return {"solution": solution, "model_name": self.model_name}
            
            elif self.api_provider == "anthropic" and "anthropic" in self.clients:
                response = self.clients["anthropic"].messages.create(
                    model=self.model_path,
                    messages=[{
                        "role": "user",
                        "content": f"""
                        PROBLEM: {problem}
                        INSTRUCTIONS:
                        - Provide a clear, concise solution in one sentence.
                        - Include brief reasoning in one additional sentence.
                        - Do not repeat the solution or add extraneous text.
                        """
                    }]
                )
                solution = response.content[0].text.strip()
                return {"solution": solution, "model_name": self.model_name}
                
            elif self.api_provider == "openai" and "openai" in self.clients:
                response = self.clients["openai"].chat.completions.create(
                    model=self.model_path,
                    max_tokens=100,
                    messages=[{
                        "role": "user",
                        "content": f"""
                        PROBLEM: {problem}
                        INSTRUCTIONS:
                        - Provide a clear, concise solution in one sentence.
                        - Include brief reasoning in one additional sentence.
                        - Do not repeat the solution or add extraneous text.
                        - Keep the response under 100 characters.
                        """
                    }]
                )
                solution = response.choices[0].message.content.strip()
                return {"solution": solution, "model_name": self.model_name}
                
            elif self.api_provider == "huggingface" and self.inference:
                prompt = f"""
                PROBLEM: {problem}
                INSTRUCTIONS:
                - Provide a clear, concise solution in one sentence.
                - Include brief reasoning in one additional sentence.
                - Do not repeat the solution or add extraneous text.
                - Keep the response under 100 characters.
                SOLUTION AND REASONING:
                """
                result = self.inference.text_generation(
                    prompt, model=self.model_path, max_new_tokens=5000, temperature=0.5
                )
                solution = result if isinstance(result, str) else result.generated_text
                return {"solution": solution.strip(), "model_name": self.model_name}
                
            elif self.api_provider == "gemini" and "gemini" in self.clients:
                model = self.clients["gemini"].GenerativeModel(self.model_path)
                try:
                    response = model.generate_content(
                        f"""
                        PROBLEM: {problem}
                        INSTRUCTIONS:
                        - Provide a clear, concise solution in one sentence.
                        - Include brief reasoning in one additional sentence.
                        - Do not repeat the solution or add extraneous text.
                        - Keep the response under 100 characters.
                        """,
                        generation_config=genai.types.GenerationConfig(
                            temperature=0.5,
                        )
                    )
                    # Check response validity and handle different response structures
                    try:
                        # First try to access text directly if available
                        if hasattr(response, 'text'):
                            solution = response.text.strip()
                        # Otherwise check for candidates
                        elif hasattr(response, 'candidates') and response.candidates:
                            # Make sure we have candidates and parts before accessing
                            if hasattr(response.candidates[0], 'content') and hasattr(response.candidates[0].content, 'parts'):
                                solution = response.candidates[0].content.parts[0].text.strip()
                            else:
                                logger.warning(f"Gemini response has candidates but missing content structure: {response}")
                                solution = "Error parsing API response; incomplete response structure."
                        else:
                            # Fallback for when candidates is empty
                            logger.warning(f"Gemini API returned no candidates: {response}")
                            solution = "No solution generated; API returned empty response."
                    except Exception as e:
                        logger.error(f"Error extracting text from Gemini response: {e}, response: {response}")
                        solution = "Error parsing API response."
                except Exception as e:
                    logger.error(f"Gemini API call failed: {e}")
                    solution = f"API error: {str(e)}"
                return {"solution": solution, "model_name": self.model_name}
                
            else:
                return {"solution": f"Error: Missing API configuration for {self.api_provider}", "model_name": self.model_name}
                
        except Exception as e:
            logger.error(f"Error in {self.model_name}: {str(e)}")
            return {"solution": f"Error: {str(e)}", "model_name": self.model_name}
    async def evaluate_solutions(self, problem: str, solutions: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Evaluate solutions from solver agents"""
        try:
            prompt = f"""
            PROBLEM: {problem}
            SOLUTIONS:
            1. {solutions[0]['model_name']}: {solutions[0]['solution']}
            2. {solutions[1]['model_name']}: {solutions[1]['solution']}
            INSTRUCTIONS:
            - Extract the numerical final answer from each solution (e.g., 68 from '16 + 52 = 68').
            - Extract the key reasoning steps from each solution.
            - Apply strict evaluation criteria:
              * Numerical answers must match EXACTLY (including units and precision).
              * Key reasoning steps must align in approach and logic.
            - Output exactly: 'AGREEMENT: YES' if BOTH the numerical answers AND reasoning align perfectly.
            - Output 'AGREEMENT: NO' followed by a one-sentence explanation if either the answers or reasoning differ in ANY way.
            - Be conservative in declaring agreement - when in doubt, declare disagreement.
            - Do not add scoring, commentary, or extraneous text.
            EVALUATION:
            """
            
            if self.api_provider == "gemini" and "gemini" in self.clients:
                # Instantiate the model for consistency and clarity
                model = self.clients["gemini"].GenerativeModel(self.model_path)
                # Use generate_content on the model instance
                response = model.generate_content(
                    prompt,
                    generation_config=genai.types.GenerationConfig(
                        temperature=0.5,
                    )
                )
                
                # Handle potential empty response or missing text attribute
                try:
                    # First try to access text directly if available
                    if hasattr(response, 'text'):
                        judgment = response.text.strip()
                    # Otherwise check for candidates
                    elif hasattr(response, 'candidates') and response.candidates:
                        # Make sure we have candidates and parts before accessing
                        if hasattr(response.candidates[0], 'content') and hasattr(response.candidates[0].content, 'parts'):
                            judgment = response.candidates[0].content.parts[0].text.strip()
                        else:
                            logger.warning(f"Gemini response has candidates but missing content structure: {response}")
                            judgment = "AGREEMENT: NO - Unable to evaluate due to API response structure issue."
                    else:
                        # Fallback for when candidates is empty
                        logger.warning(f"Empty response from Gemini API: {response}")
                        judgment = "AGREEMENT: NO - Unable to evaluate due to API response issue."
                except Exception as e:
                    logger.error(f"Error extracting text from Gemini response: {e}")
                    judgment = "AGREEMENT: NO - Unable to evaluate due to API response issue."
                
                return {"judgment": judgment, "reprompt_needed": "AGREEMENT: NO" in judgment.upper()}
                
            elif self.api_provider == "openai" and "openai" in self.clients:
                response = self.clients["openai"].chat.completions.create(
                    model=self.model_path,
                    max_tokens=200,
                    messages=[{"role": "user", "content": prompt}]
                )
                judgment = response.choices[0].message.content.strip()
                return {"judgment": judgment, "reprompt_needed": "AGREEMENT: NO" in judgment.upper()}
                
            elif self.api_provider == "huggingface" and self.inference:
                result = self.inference.text_generation(
                    prompt, model=self.model_path, max_new_tokens=200, temperature=0.5
                )
                judgment = result if isinstance(result, str) else result.generated_text
                return {"judgment": judgment.strip(), "reprompt_needed": "AGREEMENT: NO" in judgment.upper()}
                
            else:
                return {"judgment": f"Error: Missing API configuration for {self.api_provider}", "reprompt_needed": False}
                
        except Exception as e:
            logger.error(f"Error in judge: {str(e)}")
            return {"judgment": f"Error: {str(e)}", "reprompt_needed": False}

    async def reprompt_with_context(self, problem: str, solutions: List[Dict[str, Any]], judgment: str) -> Dict[str, Any]:
        """Generate a revised solution based on previous solutions and judgment"""
        try:
            prompt = f"""
            PROBLEM: {problem}
            PREVIOUS SOLUTIONS:
            1. {solutions[0]['model_name']}: {solutions[0]['solution']}
            2. {solutions[1]['model_name']}: {solutions[1]['solution']}
            JUDGE FEEDBACK: {judgment}
            INSTRUCTIONS:
            - Provide a revised, concise solution in one sentence.
            - Include brief reasoning in one additional sentence.
            - Address the judge's feedback.
            """
            
            if self.api_provider == "cohere" and "cohere" in self.clients:
                response = self.clients["cohere"].chat(
                    model=self.model_path,
                    message=prompt
                )
                solution = response.text.strip()
                return {"solution": solution, "model_name": self.model_name}
                
            elif self.api_provider == "anthropic" and "anthropic" in self.clients:
                response = self.clients["anthropic"].messages.create(
                    model=self.model_path,
                    max_tokens=100,
                    messages=[{"role": "user", "content": prompt}]
                )
                solution = response.content[0].text.strip()
                return {"solution": solution, "model_name": self.model_name}
                
            elif self.api_provider == "openai" and "openai" in self.clients:
                response = self.clients["openai"].chat.completions.create(
                    model=self.model_path,
                    max_tokens=100,
                    messages=[{"role": "user", "content": prompt}]
                )
                solution = response.choices[0].message.content.strip()
                return {"solution": solution, "model_name": self.model_name}
                
            elif self.api_provider == "huggingface" and self.inference:
                prompt += "\nREVISED SOLUTION AND REASONING:"
                result = self.inference.text_generation(
                    prompt, model=self.model_path, max_new_tokens=500, temperature=0.5
                )
                solution = result if isinstance(result, str) else result.generated_text
                return {"solution": solution.strip(), "model_name": self.model_name}
                
            elif self.api_provider == "gemini" and "gemini" in self.clients:
                # Instantiate the model for consistency and clarity
                model = self.clients["gemini"].GenerativeModel(self.model_path)
                # Use generate_content
                response = model.generate_content(
                    f"""
                    PROBLEM: {problem}
                    PREVIOUS SOLUTIONS:
                    1. {solutions[0]['model_name']}: {solutions[0]['solution']}
                    2. {solutions[1]['model_name']}: {solutions[1]['solution']}
                    JUDGE FEEDBACK: {judgment}
                    INSTRUCTIONS:
                    - Provide a revised, concise solution in one sentence.
                    - Include brief reasoning in one additional sentence.
                    - Address the judge's feedback.
                    """,
                     generation_config=genai.types.GenerationConfig(
                        temperature=0.5,
                        max_output_tokens=100
                    )
                )
                # Handle potential empty response or missing text attribute
                try:
                    # First try to access text directly if available
                    if hasattr(response, 'text'):
                        solution = response.text.strip()
                    # Otherwise check for candidates
                    elif hasattr(response, 'candidates') and response.candidates:
                        # Make sure we have candidates and parts before accessing
                        if hasattr(response.candidates[0], 'content') and hasattr(response.candidates[0].content, 'parts'):
                            solution = response.candidates[0].content.parts[0].text.strip()
                        else:
                            logger.warning(f"Gemini response has candidates but missing content structure: {response}")
                            solution = "Unable to generate a solution due to API response structure issue."
                    else:
                        # Fallback for when candidates is empty
                        logger.warning(f"Empty response from Gemini API: {response}")
                        solution = "Unable to generate a solution due to API response issue."
                except Exception as e:
                    logger.error(f"Error extracting text from Gemini response: {e}")
                    solution = "Unable to generate a solution due to API response issue."
                
                return {"solution": solution, "model_name": self.model_name}
            else:
                return {"solution": f"Error: Missing API configuration for {self.api_provider}", "model_name": self.model_name}
                
        except Exception as e:
            logger.error(f"Error in {self.model_name}: {str(e)}")
            return {"solution": f"Error: {str(e)}", "model_name": self.model_name}

# --- Model Registry ---
class ModelRegistry:
    @staticmethod
    def get_available_models():
        """Get the list of available models grouped by provider (original list)"""
        return {
            "Anthropic": [
                {"name": "Claude 3.5 Sonnet", "id": "claude-3-5-sonnet-20240620", "provider": "anthropic", "type": ["solver"], "icon": "📜"},
                {"name": "Claude 3.7 Sonnet", "id": "claude-3-7-sonnet-20250219", "provider": "anthropic", "type": ["solver"], "icon": "📜"},
                {"name": "Claude 3 Opus", "id": "claude-3-opus-20240229", "provider": "anthropic", "type": ["solver"], "icon": "📜"},
                {"name": "Claude 3 Haiku", "id": "claude-3-haiku-20240307", "provider": "anthropic", "type": ["solver"], "icon": "📜"}
            ],
            "OpenAI": [
                {"name": "GPT-4o", "id": "gpt-4o", "provider": "openai", "type": ["solver"], "icon": "🤖"},
                {"name": "GPT-4 Turbo", "id": "gpt-4-turbo", "provider": "openai", "type": ["solver"], "icon": "🤖"},
                {"name": "GPT-4", "id": "gpt-4", "provider": "openai", "type": ["solver"], "icon": "🤖"},
                {"name": "GPT-3.5 Turbo", "id": "gpt-3.5-turbo", "provider": "openai", "type": ["solver"], "icon": "🤖"},
                {"name": "OpenAI o1", "id": "o1", "provider": "openai", "type": ["solver", "judge"], "icon": "🤖"},
                {"name": "OpenAI o3", "id": "o3", "provider": "openai", "type": ["solver", "judge"], "icon": "🤖"}
            ],
            "Cohere": [
                {"name": "Cohere Command R", "id": "command-r-08-2024", "provider": "cohere", "type": ["solver"], "icon": "💬"},
                {"name": "Cohere Command R+", "id": "command-r-plus-08-2024", "provider": "cohere", "type": ["solver"], "icon": "💬"} 
            ],
            "Google": [
                {"name": "Gemini 1.5 Pro", "id": "gemini-1.5-pro", "provider": "gemini", "type": ["solver"], "icon": "🌟"},
                {"name": "Gemini 2.0 Flash Thinking Experimental 01-21", "id": "gemini-2.0-flash-thinking-exp-01-21", "provider": "gemini", "type": ["solver", "judge"], "icon": "🌟"},
                {"name": "Gemini 2.5 Pro Experimental 03-25", "id": "gemini-2.5-pro-exp-03-25", "provider": "gemini", "type": ["solver", "judge"], "icon": "🌟"}
            ],
            "HuggingFace": [
                {"name": "Llama 3.3 70B Instruct", "id": "meta-llama/Llama-3.3-70B-Instruct", "provider": "huggingface", "type": ["solver"], "icon": "🔥"},
                {"name": "Llama 3.2 3B Instruct", "id": "meta-llama/Llama-3.2-3B-Instruct", "provider": "huggingface", "type": ["solver"], "icon": "🔥"},
                {"name": "Llama 3.1 70B Instruct", "id": "meta-llama/Llama-3.1-70B-Instruct", "provider": "huggingface", "type": ["solver"], "icon": "🔥"},
                {"name": "Mistral 7B Instruct v0.3", "id": "mistralai/Mistral-7B-Instruct-v0.3", "provider": "huggingface", "type": ["solver"], "icon": "🔥"},
                {"name": "DeepSeek R1 Distill Qwen 32B", "id": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", "provider": "huggingface", "type": ["solver", "judge"], "icon": "🔥"},
                {"name": "DeepSeek Coder V2 Instruct", "id": "deepseek-ai/DeepSeek-Coder-V2-Instruct", "provider": "huggingface", "type": ["solver"], "icon": "🔥"},
                {"name": "Qwen 2.5 72B Instruct", "id": "Qwen/Qwen2.5-72B-Instruct", "provider": "huggingface", "type": ["solver"], "icon": "🔥"},
                {"name": "Qwen 2.5 Coder 32B Instruct", "id": "Qwen/Qwen2.5-Coder-32B-Instruct", "provider": "huggingface", "type": ["solver"], "icon": "🔥"},
                {"name": "Qwen 2.5 Math 1.5B Instruct", "id": "Qwen/Qwen2.5-Math-1.5B-Instruct", "provider": "huggingface", "type": ["solver"], "icon": "🔥"},
                {"name": "Gemma 3 27B Instruct", "id": "google/gemma-3-27b-it", "provider": "huggingface", "type": ["solver"], "icon": "🔥"},
                {"name": "Phi-3 Mini 4K Instruct", "id": "microsoft/Phi-3-mini-4k-instruct", "provider": "huggingface", "type": ["solver"], "icon": "🔥"}
            ]
        }
    
    @staticmethod
    def get_solver_models():
        """Get models suitable for solver role with provider grouping"""
        all_models = ModelRegistry.get_available_models()
        solver_models = {}
        
        for provider, models in all_models.items():
            provider_models = []
            for model in models:
                if "solver" in model["type"]:
                    provider_models.append({
                        "name": f"{model['icon']} {model['name']} ({provider})",
                        "id": model["id"],
                        "provider": model["provider"]
                    })
            if provider_models:
                solver_models[provider] = provider_models
        
        return solver_models
    
    @staticmethod
    def get_judge_models():
        """Get only specific reasoning models suitable for judge role with provider grouping"""
        all_models = ModelRegistry.get_available_models()
        judge_models = {}
        allowed_judge_models = [
            "Gemini 2.0 Flash Thinking Experimental 01-21 (Google)",
            "DeepSeek R1 (HuggingFace)",
            "Gemini 2.5 Pro Experimental 03-25 (Google)",
            "OpenAI o1 (OpenAI)",
            "OpenAI o3 (OpenAI)"
        ]
        
        for provider, models in all_models.items():
            provider_models = []
            for model in models:
                full_name = f"{model['name']} ({provider})"
                if "judge" in model["type"] and full_name in allowed_judge_models:
                    provider_models.append({
                        "name": f"{model['icon']} {model['name']} ({provider})",
                        "id": model["id"],
                        "provider": model["provider"]
                    })
            if provider_models:
                judge_models[provider] = provider_models
        
        return judge_models

# --- Orchestrator Class ---
class PolyThinkOrchestrator:
    def __init__(self, solver1_config=None, solver2_config=None, judge_config=None, api_clients=None):
        self.solvers = []
        self.judge = None
        self.api_clients = api_clients or {}
        
        if solver1_config:
            solver1 = PolyThinkAgent(
                model_name=solver1_config["name"].split(" ", 1)[1].rsplit(" (", 1)[0] if " " in solver1_config["name"] else solver1_config["name"],
                model_path=solver1_config["id"],
                api_provider=solver1_config["provider"]
            )
            solver1.set_clients(self.api_clients)
            self.solvers.append(solver1)
            
        if solver2_config:
            solver2 = PolyThinkAgent(
                model_name=solver2_config["name"].split(" ", 1)[1].rsplit(" (", 1)[0] if " " in solver2_config["name"] else solver2_config["name"],
                model_path=solver2_config["id"],
                api_provider=solver2_config["provider"]
            )
            solver2.set_clients(self.api_clients)
            self.solvers.append(solver2)
            
        if judge_config:
            self.judge = PolyThinkAgent(
                model_name=judge_config["name"].split(" ", 1)[1].rsplit(" (", 1)[0] if " " in judge_config["name"] else judge_config["name"],
                model_path=judge_config["id"],
                role="judge",
                api_provider=judge_config["provider"]
            )
            self.judge.set_clients(self.api_clients)

    async def get_initial_solutions(self, problem: str) -> List[Dict[str, Any]]:
        tasks = [solver.solve_problem(problem) for solver in self.solvers]
        return await asyncio.gather(*tasks)

    async def get_judgment(self, problem: str, solutions: List[Dict[str, Any]]) -> Dict[str, Any]:
        if self.judge:
            return await self.judge.evaluate_solutions(problem, solutions)
        return {"judgment": "No judge configured", "reprompt_needed": False}

    async def get_revised_solutions(self, problem: str, solutions: List[Dict[str, Any]], judgment: str) -> List[Dict[str, Any]]:
        tasks = [solver.reprompt_with_context(problem, solutions, judgment) for solver in self.solvers]
        return await asyncio.gather(*tasks)

    def generate_final_report(self, problem: str, history: List[Dict[str, Any]]) -> str:
        report = f"""
        <div class="final-report-container">
            <h2 class="final-report-title">🔍 Final Analysis Report</h2>
            <div class="problem-container">
                <h3 class="problem-title">Problem Statement</h3>
                <div class="problem-content">{problem}</div>
            </div>
        """

        # Add best answer section if there's agreement
        last_judgment = next((step.get("judgment", "") for step in reversed(history) if "judgment" in step), "")
        if "AGREEMENT: YES" in last_judgment.upper():
            # Get the last solutions before agreement
            last_solutions = next((step["solutions"] for step in reversed(history) if "solutions" in step), None)
            if last_solutions:
                report += f"""
                <div class="best-answer-container agreement">
                    <h3>Best Answer</h3>
                    <div class="best-answer-content">
                        <div class="best-answer-icon">✨</div>
                        <div class="best-answer-text">
                            <p><strong>Agreed Solution:</strong> {last_solutions[0]['solution']}</p>
                            <p><strong>Models:</strong> {last_solutions[0]['model_name']} & {last_solutions[1]['model_name']}</p>
                        </div>
                    </div>
                </div>
                """
            
        report += """
            <div class="timeline-container">
        """

        for i, step in enumerate(history, 1):
            if "solutions" in step and i == 1:
                report += f"""
                <div class="timeline-item">
                    <div class="timeline-marker">1</div>
                    <div class="timeline-content">
                        <h4>Initial Solutions</h4>
                        <div class="solutions-container">
                """
                
                for sol in step["solutions"]:
                    report += f"""
                    <div class="solution-item">
                        <div class="solution-header">{sol['model_name']}</div>
                        <div class="solution-body">{sol['solution']}</div>
                    </div>
                    """
                
                report += """
                        </div>
                    </div>
                </div>
                """
            
            elif "judgment" in step:
                is_agreement = "AGREEMENT: YES" in step["judgment"].upper()
                judgment_class = "agreement" if is_agreement else "disagreement"
                judgment_icon = "✅" if is_agreement else "❌"
                
                report += f"""
                <div class="timeline-item">
                    <div class="timeline-marker">{i}</div>
                    <div class="timeline-content">
                        <h4>Evaluation {(i+1)//2}</h4>
                        <div class="judgment-container {judgment_class}">
                            <div class="judgment-icon">{judgment_icon}</div>
                            <div class="judgment-text">{step["judgment"]}</div>
                        </div>
                    </div>
                </div>
                """
            
            elif "solutions" in step and i > 1:
                round_num = (i+1)//2
                report += f"""
                <div class="timeline-item">
                    <div class="timeline-marker">{i}</div>
                    <div class="timeline-content">
                        <h4>Revised Solutions (Round {round_num})</h4>
                        <div class="solutions-container">
                """
                
                for sol in step["solutions"]:
                    report += f"""
                    <div class="solution-item">
                        <div class="solution-header">{sol['model_name']}</div>
                        <div class="solution-body">{sol['solution']}</div>
                    </div>
                    """
                
                report += """
                        </div>
                    </div>
                </div>
                """

        last_judgment = next((step.get("judgment", "") for step in reversed(history) if "judgment" in step), "")
        if "AGREEMENT: YES" in last_judgment.upper():
            confidence = "100%" if len(history) == 2 else "80%"
            report += f"""
            <div class="conclusion-container agreement">
                <h3>Conclusion</h3>
                <div class="conclusion-content">
                    <div class="conclusion-icon">✅</div>
                    <div class="conclusion-text">
                        <p>Models reached <strong>AGREEMENT</strong></p>
                        <p>Confidence level: <strong>{confidence}</strong></p>
                    </div>
                </div>
            </div>
            """
        else:
            report += f"""
            <div class="conclusion-container disagreement">
                <h3>Conclusion</h3>
                <div class="conclusion-content">
                    <div class="conclusion-icon">❓</div>
                    <div class="conclusion-text">
                        <p>Models could not reach agreement</p>
                        <p>Review all solutions above for best answer</p>
                    </div>
                </div>
            </div>
            """
        
        report += """
            </div>
        </div>
        """
        
        return report

# --- Gradio Interface ---
def create_polythink_interface():
    custom_css = """
    /* Reverted to Original Black Theme */
    body {
        background: #000000;
        color: #ffffff;
        font-family: 'Arial', sans-serif;
    }
    .gradio-container {
        background: #1a1a1a;
        border-radius: 10px;
        box-shadow: 0 4px 15px rgba(0, 0, 0, 0.5);
        padding: 20px;
    }
    .gr-button {
        background: linear-gradient(45deg, #666666, #999999);
        color: #ffffff;
        border: none;
        padding: 10px 20px;
        border-radius: 5px;
        transition: all 0.3s ease;
    }
    .gr-button:hover {
        background: linear-gradient(45deg, #555555, #888888);
        transform: translateY(-2px);
    }
    .gr-textbox {
        background: #333333;
        color: #ffffff;
        border: 1px solid #444444;
        border-radius: 5px;
        padding: 10px;
    }
    .gr-slider {
        background: #333333;
        border-radius: 5px;
    }
    .gr-slider .track-fill {
        background: #cccccc;
    }
    .step-section {
        background: #1a1a1a;
        border-radius: 8px;
        padding: 15px;
        margin-bottom: 20px;
        box-shadow: 0 2px 10px rgba(0, 0, 0, 0.3);
    }
    .step-section h3 {
        color: #cccccc;
        margin-top: 0;
        font-size: 1.5em;
    }
    .step-section p {
        color: #aaaaaa;
        line-height: 1.6;
    }
    .step-section code {
        background: #333333;
        padding: 2px 6px;
        border-radius: 3px;
        color: #ff6b6b;
    }
    .step-section strong {
        color: #ffffff;
    }
    .status-bar {
        background: #1a1a1a;
        padding: 10px;
        border-radius: 5px;
        font-size: 1.1em;
        margin-bottom: 20px;
        border-left: 4px solid #666666;
    }
    
    /* Agreement/Disagreement styling */
    .agreement {
        color: #4CAF50 !important;
        border: 1px solid #4CAF50;
        background-color: rgba(76, 175, 80, 0.1) !important;
        padding: 10px;
        border-radius: 5px;
    }
    
    .disagreement {
        color: #F44336 !important;
        border: 1px solid #F44336;
        background-color: rgba(244, 67, 54, 0.1) !important;
        padding: 10px;
        border-radius: 5px;
    }
    
    /* Enhanced Final Report Styling */
    .final-report {
        background: #111111;
        padding: 0;
        border-radius: 8px;
        box-shadow: 0 4px 15px rgba(0, 0, 0, 0.5);
        margin-top: 20px;
        overflow: hidden;
    }
    
    .final-report-container {
        font-family: 'Arial', sans-serif;
    }
    
    .final-report-title {
        background: linear-gradient(45deg, #333333, #444444);
        color: #ffffff;
        padding: 20px;
        margin: 0;
        border-bottom: 1px solid #555555;
        font-size: 24px;
        text-align: center;
    }
    
    .problem-container {
        background: #222222;
        padding: 15px 20px;
        margin: 0;
        border-bottom: 1px solid #333333;
    }
    
    .problem-title {
        color: #bbbbbb;
        margin: 0 0 10px 0;
        font-size: 18px;
    }
    
    .problem-content {
        background: #333333;
        padding: 15px;
        border-radius: 5px;
        font-family: monospace;
        font-size: 16px;
        color: #ffffff;
    }
    
    .timeline-container {
        padding: 20px;
    }
    
    .timeline-item {
        display: flex;
        margin-bottom: 25px;
        position: relative;
    }
    
    .timeline-item:before {
        content: '';
        position: absolute;
        left: 15px;
        top: 30px;
        bottom: -25px;
        width: 2px;
        background: #444444;
        z-index: 0;
    }
    
    .timeline-item:last-child:before {
        display: none;
    }
    
    .timeline-marker {
        width: 34px;
        height: 34px;
        border-radius: 50%;
        background: #333333;
        display: flex;
        align-items: center;
        justify-content: center;
        font-weight: bold;
        position: relative;
        z-index: 1;
        border: 2px solid #555555;
        margin-right: 15px;
    }
    
    .timeline-content {
        flex: 1;
        background: #1d1d1d;
        border-radius: 5px;
        padding: 15px;
        border: 1px solid #333333;
    }
    
    .timeline-content h4 {
        margin-top: 0;
        margin-bottom: 15px;
        color: #cccccc;
        border-bottom: 1px solid #333333;
        padding-bottom: 8px;
    }
    
    .solutions-container {
        display: flex;
        flex-wrap: wrap;
        gap: 10px;
    }
    
    .solution-item {
        flex: 1;
        min-width: 250px;
        background: #252525;
        border-radius: 5px;
        overflow: hidden;
        border: 1px solid #383838;
    }
    
    .solution-header {
        background: #333333;
        padding: 8px 12px;
        font-weight: bold;
        color: #dddddd;
        border-bottom: 1px solid #444444;
    }
    
    .solution-body {
        padding: 12px;
        color: #bbbbbb;
    }
    
    .judgment-container {
        display: flex;
        align-items: center;
        padding: 10px;
        border-radius: 5px;
    }
    
    .judgment-icon {
        font-size: 24px;
        margin-right: 15px;
    }
    
    .conclusion-container {
        margin-top: 30px;
        border-radius: 5px;
        padding: 5px 15px 15px;
    }
    
    .conclusion-content {
        display: flex;
        align-items: center;
    }
    
    .conclusion-icon {
        font-size: 36px;
        margin-right: 20px;
    }
    
    .conclusion-text {
        flex: 1;
    }
    
    .conclusion-text p {
        margin: 5px 0;
    }
    
    /* Header styling */
    .app-header {
        background: linear-gradient(45deg, #222222, #333333);
        padding: 20px;
        border-radius: 10px;
        margin-bottom: 20px;
        box-shadow: 0 4px 10px rgba(0, 0, 0, 0.3);
        border: 1px solid #444444;
    }
    
    .app-title {
        font-size: 28px;
        margin: 0 0 10px 0;
        background: -webkit-linear-gradient(45deg, #cccccc, #ffffff);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        display: inline-block;
    }
    
    .app-subtitle {
        font-size: 16px;
        color: #aaaaaa;
        margin: 0;
    }
    
    /* Button style */
    .primary-button {
        background: linear-gradient(45deg, #555555, #777777) !important;
        border: none !important;
        color: white !important;
        padding: 12px 24px !important;
        font-weight: bold !important;
        transition: all 0.3s ease !important;
        box-shadow: 0 4px 10px rgba(0, 0, 0, 0.3) !important;
    }
    
    .primary-button:hover {
        transform: translateY(-2px) !important;
        box-shadow: 0 6px 15px rgba(0, 0, 0, 0.4) !important;
        background: linear-gradient(45deg, #666666, #888888) !important;
    }
    
    /* Best Answer styling */
    .best-answer-container {
        background: #1a1a1a;
        border-radius: 8px;
        padding: 20px;
        margin: 20px 0;
        box-shadow: 0 4px 15px rgba(0, 0, 0, 0.5);
        border: 1px solid #4CAF50;
    }
    
    .best-answer-container h3 {
        color: #4CAF50;
        margin-top: 0;
        margin-bottom: 15px;
        font-size: 1.5em;
    }
    
    .best-answer-content {
        display: flex;
        align-items: flex-start;
        gap: 15px;
    }
    
    .best-answer-icon {
        font-size: 24px;
        color: #4CAF50;
    }
    
    .best-answer-text {
        flex: 1;
    }
    
    .best-answer-text p {
        margin: 5px 0;
        color: #ffffff;
    }
    
    .best-answer-text strong {
        color: #4CAF50;
    }
    """

    # Hardcoded model configurations
    solver1_config = {
        "name": "Cohere Command R",
        "id": "command-r-08-2024",
        "provider": "cohere"
    }
    
    solver2_config = {
        "name": "Llama 3.2 3B Instruct",
        "id": "meta-llama/Llama-3.2-3B-Instruct",
        "provider": "huggingface"
    }
    
    judge_config = {
        "name": "Gemini 2.0 Flash Thinking Experimental 01-21",
        "id": "gemini-2.0-flash-thinking-exp-01-21",
        "provider": "gemini"
    }

    async def solve_problem(problem: str, max_rounds: int):
        # Get API keys from environment variables or Hugging Face secrets
        api_clients = {}
        
        # Cohere client
        cohere_key = os.getenv("COHERE_API_KEY")
        if cohere_key:
            api_clients["cohere"] = cohere.Client(cohere_key)
            
        # Hugging Face client
        hf_key = os.getenv("HF_API_KEY")
        if hf_key:
            api_clients["huggingface"] = hf_key
            
        # Gemini client
        gemini_key = os.getenv("GEMINI_API_KEY")
        if gemini_key:
            genai.configure(api_key=gemini_key)
            api_clients["gemini"] = genai
            
        # Anthropic client
        anthropic_key = os.getenv("ANTHROPIC_API_KEY")
        if anthropic_key:
            api_clients["anthropic"] = Anthropic(api_key=anthropic_key)
            
        # OpenAI client
        openai_key = os.getenv("OPENAI_API_KEY")
        if openai_key:
            api_clients["openai"] = openai.OpenAI(api_key=openai_key)

        # Check if all required API keys are present
        required_providers = {solver1_config["provider"], solver2_config["provider"], judge_config["provider"]}
        missing_keys = [p for p in required_providers if p not in api_clients]
        if missing_keys:
            yield [
                gr.update(value=f"Error: Missing API keys for {', '.join(missing_keys)}", visible=True),
                gr.update(visible=False),
                gr.update(visible=False),
                gr.update(visible=False),
                gr.update(visible=False),
                gr.update(visible=False),
                gr.update(visible=False),
                gr.update(visible=False),
                gr.update(value=f"### Status: ❌ Missing API keys for {', '.join(missing_keys)}", visible=True)
            ]
            return

        orchestrator = PolyThinkOrchestrator(solver1_config, solver2_config, judge_config, api_clients)

        initial_solutions = await orchestrator.get_initial_solutions(problem)
        initial_content = f"## Initial Solutions\n**Problem:** `{problem}`\n\n**Solutions:**\n- **{initial_solutions[0]['model_name']}**: {initial_solutions[0]['solution']}\n- **{initial_solutions[1]['model_name']}**: {initial_solutions[1]['solution']}"
        yield [
            gr.update(value=initial_content, visible=True),
            gr.update(value="", visible=False),
            gr.update(value="", visible=False),
            gr.update(value="", visible=False),
            gr.update(value="", visible=False),
            gr.update(value="", visible=False),
            gr.update(value="", visible=False),
            gr.update(value="", visible=False),
            gr.update(value="### Status: 📋 Initial solutions generated", visible=True)
        ]
        await asyncio.sleep(1)

        solutions = initial_solutions
        history = [{"solutions": initial_solutions}]
        max_outputs = max(int(max_rounds) * 2, 6)
        round_outputs = [""] * max_outputs

        for round_num in range(int(max_rounds)):
            judgment = await orchestrator.get_judgment(problem, solutions)
            history.append({"judgment": judgment["judgment"]})
            
            is_agreement = "AGREEMENT: YES" in judgment["judgment"].upper()
            agreement_class = "agreement" if is_agreement else "disagreement"
            agreement_icon = "✅" if is_agreement else "❌"
            
            judgment_content = f"## Round {round_num + 1} Judgment\n**Evaluation:** <div class='{agreement_class}'>{agreement_icon} {judgment['judgment']}</div>"
            round_outputs[round_num * 2] = judgment_content
            
            yield [
                gr.update(value=initial_content, visible=True),
                gr.update(value=round_outputs[0], visible=bool(round_outputs[0])),
                gr.update(value=round_outputs[1], visible=bool(round_outputs[1])),
                gr.update(value=round_outputs[2], visible=bool(round_outputs[2])),
                gr.update(value=round_outputs[3], visible=bool(round_outputs[3])),
                gr.update(value=round_outputs[4], visible=bool(round_outputs[4])),
                gr.update(value=round_outputs[5], visible=bool(round_outputs[5])),
                gr.update(value="", visible=False),
                gr.update(value=f"### Status: 🔍 Round {round_num + 1} judgment complete", visible=True)
            ]
            await asyncio.sleep(1)

            if not judgment["reprompt_needed"]:
                break

            revised_solutions = await orchestrator.get_revised_solutions(problem, solutions, judgment["judgment"])
            history.append({"solutions": revised_solutions})
            revision_content = f"## Round {round_num + 1} Revised Solutions\n**Revised Solutions:**\n- **{revised_solutions[0]['model_name']}**: {revised_solutions[0]['solution']}\n- **{revised_solutions[1]['model_name']}**: {revised_solutions[1]['solution']}"
            round_outputs[round_num * 2 + 1] = revision_content
            yield [
                gr.update(value=initial_content, visible=True),
                gr.update(value=round_outputs[0], visible=bool(round_outputs[0])),
                gr.update(value=round_outputs[1], visible=bool(round_outputs[1])),
                gr.update(value=round_outputs[2], visible=bool(round_outputs[2])),
                gr.update(value=round_outputs[3], visible=bool(round_outputs[3])),
                gr.update(value=round_outputs[4], visible=bool(round_outputs[4])),
                gr.update(value=round_outputs[5], visible=bool(round_outputs[5])),
                gr.update(value="", visible=False),
                gr.update(value=f"### Status: 🔄 Round {round_num + 1} revised solutions generated", visible=True)
            ]
            await asyncio.sleep(1)
            solutions = revised_solutions

        final_report_content = orchestrator.generate_final_report(problem, history)
        yield [
            gr.update(value=initial_content, visible=True),
            gr.update(value=round_outputs[0], visible=True),
            gr.update(value=round_outputs[1], visible=bool(round_outputs[1])),
            gr.update(value=round_outputs[2], visible=bool(round_outputs[2])),
            gr.update(value=round_outputs[3], visible=bool(round_outputs[3])),
            gr.update(value=round_outputs[4], visible=bool(round_outputs[4])),
            gr.update(value=round_outputs[5], visible=bool(round_outputs[5])),
            gr.update(value=final_report_content, visible=True),
            gr.update(value=f"### Status: ✨ Process complete! Completed {round_num + 1} round(s)", visible=True)
        ]

    with gr.Blocks(title="PolyThink Alpha", css=custom_css) as demo:
        with gr.Column(elem_classes=["app-header"]):
            gr.Markdown("<h1 class='app-title'>PolyThink Alpha</h1>", show_label=False)
            gr.Markdown("<p class='app-subtitle'>Multi-Agent Problem Solving System</p>", show_label=False)

        with gr.Row():
            with gr.Column(scale=2):
                gr.Markdown("### Problem Input")
                problem_input = gr.Textbox(label="Problem", placeholder="e.g., What is 32 + 63?", lines=3)
                rounds_slider = gr.Slider(2, 6, value=2, step=1, label="Maximum Rounds")
                solve_button = gr.Button("Solve Problem", elem_classes=["primary-button"])

        status_text = gr.Markdown("### Status: Ready", elem_classes=["status-bar"], visible=True)

        with gr.Column():
            initial_solutions = gr.Markdown(elem_classes=["step-section"], visible=False)
            round_judgment_1 = gr.Markdown(elem_classes=["step-section"], visible=False)
            revised_solutions_1 = gr.Markdown(elem_classes=["step-section"], visible=False)
            round_judgment_2 = gr.Markdown(elem_classes=["step-section"], visible=False)
            revised_solutions_2 = gr.Markdown(elem_classes=["step-section"], visible=False)
            round_judgment_3 = gr.Markdown(elem_classes=["step-section"], visible=False)
            revised_solutions_3 = gr.Markdown(elem_classes=["step-section"], visible=False)
            final_report = gr.HTML(elem_classes=["final-report"], visible=False)

        solve_button.click(
            fn=solve_problem,
            inputs=[
                problem_input,
                rounds_slider
            ],
            outputs=[
                initial_solutions,
                round_judgment_1,
                revised_solutions_1,
                round_judgment_2,
                revised_solutions_2,
                round_judgment_3,
                revised_solutions_3,
                final_report,
                status_text
            ]
        )

    return demo.queue()

if __name__ == "__main__":
    demo = create_polythink_interface()
    demo.launch(share=True)