import os
import gradio as gr
import requests
import inspect
import pandas as pd
from dotenv import load_dotenv
from smolagents import CodeAgent, DuckDuckGoSearchTool, FinalAnswerTool, InferenceClientModel, Tool, tool, VisitWebpageTool

# Load environment variables
load_dotenv()

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Custom Tools for GAIA Dataset ---

@tool
def calculate_math(expression: str) -> str:
    """
    Calculates mathematical expressions safely.
    Args:
        expression: Mathematical expression to evaluate (e.g., "2 + 2", "sqrt(16)")
    """
    try:
        import math
        import re
        
        # Replace common math functions
        expression = expression.replace("sqrt", "math.sqrt")
        expression = expression.replace("log", "math.log")
        expression = expression.replace("sin", "math.sin")
        expression = expression.replace("cos", "math.cos")
        expression = expression.replace("tan", "math.tan")
        expression = expression.replace("pi", "math.pi")
        expression = expression.replace("e", "math.e")
        
        # Safe evaluation
        allowed_names = {
            k: v for k, v in math.__dict__.items() if not k.startswith("__")
        }
        allowed_names.update({"abs": abs, "round": round, "min": min, "max": max})
        
        result = eval(expression, {"__builtins__": {}}, allowed_names)
        return str(result)
    except Exception as e:
        return f"Error calculating: {str(e)}"

@tool
def analyze_data(data_description: str) -> str:
    """
    Analyzes data patterns, statistics, or trends described in text.
    Args:
        data_description: Description of data to analyze
    """
    # This is a simplified analysis tool
    # In a real scenario, this could connect to data analysis libraries
    return f"Data analysis for: {data_description}. Please provide specific data or use web search for current statistics."

@tool
def fact_checker(claim: str) -> str:
    """
    Helps verify factual claims by suggesting verification approaches.
    Args:
        claim: The factual claim to verify
    """
    return f"To verify '{claim}', I recommend using web search for recent, authoritative sources. Cross-reference multiple reliable sources."

class AdvancedReasoningTool(Tool):
    name = "advanced_reasoning"
    description = """
    This tool helps break down complex multi-step reasoning problems.
    It provides structured thinking for complex questions."""
    
    inputs = {
        "problem": {
            "type": "string",
            "description": "A complex problem that requires step-by-step reasoning",
        },
        "problem_type": {
            "type": "string", 
            "description": "Type of problem (e.g., 'logical', 'mathematical', 'analytical', 'research')",
            "nullable": True
        }
    }
    
    output_type = "string"

    def forward(self, problem: str, problem_type: str = None):
        if problem_type is None:
            problem_type = "general"
            
        reasoning_frameworks = {
            "logical": "1. Identify premises\n2. Apply logical rules\n3. Check for contradictions\n4. Draw conclusions",
            "mathematical": "1. Understand what's being asked\n2. Identify known values\n3. Choose appropriate formulas\n4. Calculate step-by-step\n5. Verify the answer",
            "analytical": "1. Break down into components\n2. Analyze each part\n3. Look for patterns/relationships\n4. Synthesize findings",
            "research": "1. Define research question\n2. Identify reliable sources\n3. Gather information\n4. Cross-reference facts\n5. Form conclusion"
        }
        
        framework = reasoning_frameworks.get(problem_type.lower(), reasoning_frameworks["analytical"])
        return f"Problem: {problem}\n\nSuggested approach ({problem_type}):\n{framework}"

class BasicAgent:
    def __init__(self):
        print("🤖 BasicAgent initialized with smolagents framework.")
        
        # Get HF token from environment
        hf_token = os.getenv("HUGGINGFACE_HUB_TOKEN") or os.getenv("HF_TOKEN")
        
        if not hf_token:
            raise ValueError("❌ No HF token found. Please set HF_TOKEN or HUGGINGFACE_HUB_TOKEN in your .env file\n"
                           "You can get a token from: https://huggingface.co/settings/tokens")
        
        try:
            # Initialize model with HF token
            model = InferenceClientModel(
                model_id="HuggingFaceTB/SmolLM3-3B",
                token=hf_token
            )
            
            # Create agent with comprehensive tools
            self.agent = CodeAgent(
                tools=[
                    DuckDuckGoSearchTool(),
                    VisitWebpageTool(),
                    calculate_math,
                    analyze_data,
                    fact_checker,
                    AdvancedReasoningTool(),
                    FinalAnswerTool()
                ],
                model=model,
                max_steps=15,  # Increased for complex GAIA questions
                verbosity_level=2
            )
            print("✅ SmolAgent initialized successfully with all tools")
            
        except Exception as e:
            print(f"❌ Error initializing SmolAgent: {e}")
            raise e
    
    def __call__(self, question: str) -> str:
        print(f"🤖 SmolAgent received question: {question[:100]}...")
        
        try:
            print("🔄 Running SmolAgent with tools...")
            
            # Add context to help the agent understand it should provide a final answer
            enhanced_question = f"""
            Please answer the following question thoroughly and accurately. Use the available tools to search for information, visit websites, perform calculations, or analyze data as needed.

            Question: {question}

            Please provide a clear, specific final answer at the end.
            """
            
            result = self.agent.run(enhanced_question)
            
            print("✅ SmolAgent completed successfully!")
            
            # Extract the final answer if it's wrapped in agent output
            if hasattr(result, 'content'):
                answer = result.content
            elif isinstance(result, dict) and 'output' in result:
                answer = result['output']
            else:
                answer = str(result)
            
            print(f"📝 SmolAgent returning answer: {answer[:200]}...")
            
            # Ensure we have a meaningful answer
            if not answer or answer.lower().strip() == "":
                return "I apologize, but I couldn't generate a proper response to your question."
            
            return answer
            
        except Exception as e:
            error_msg = f"❌ SmolAgent Error: {str(e)}"
            print(error_msg)
            print(f"📋 Full error details: {repr(e)}")
            
            return f"Sorry, I encountered an error while processing your question: {str(e)}"
    
    def test_connection(self):
        """Test if the agent is working properly"""
        try:
            test_response = self("What is the capital of France?")
            print(f"🧪 Test response: {test_response}")
            return True, test_response
        except Exception as e:
            print(f"🚫 Test failed: {e}")
            return False, str(e)

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID")  # Get the SPACE_ID for sending link to the code

    if profile:
        username = f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # Generate agent code URL
    if space_id:
        agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    else:
        agent_code = "https://huggingface.co/spaces/your-username/your-space/tree/main"
    
    print(f"Agent code URL: {agent_code}")

    # 1. Instantiate Agent
    try:
        print("🚀 Initializing SmolAgent...")
        agent = BasicAgent()
        
        # Test the agent before proceeding
        print("🧪 Testing agent connection...")
        test_success, test_result = agent.test_connection()
        if not test_success:
            return f"❌ Agent test failed: {test_result}\nPlease check your HF_TOKEN in environment variables.", None
        
        print(f"✅ Agent test successful: {test_result[:100]}...")
        
    except Exception as e:
        error_msg = f"❌ Error initializing agent: {e}"
        print(error_msg)
        return error_msg, None

    # 2. Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            print("Fetched questions list is empty.")
            return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except requests.exceptions.JSONDecodeError as e:
        print(f"Error decoding JSON response from questions endpoint: {e}")
        print(f"Response text: {response.text[:500]}")
        return f"Error decoding server response for questions: {e}", None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run your Agent
    results_log = []
    answers_payload = []
    print(f"Running SmolAgent on {len(questions_data)} questions...")
    
    for i, item in enumerate(questions_data):
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
        
        print(f"🔄 Processing question {i+1}/{len(questions_data)}: {task_id}")
        try:
            submitted_answer = agent(question_text)
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text, 
                "Submitted Answer": submitted_answer[:300] + "..." if len(submitted_answer) > 300 else submitted_answer
            })
            print(f"✅ Completed question {i+1}")
        except Exception as e:
            error_msg = f"AGENT ERROR: {e}"
            print(f"❌ Error running agent on task {task_id}: {e}")
            answers_payload.append({"task_id": task_id, "submitted_answer": error_msg})
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
                "Submitted Answer": error_msg
            })

    if not answers_payload:
        print("Agent did not produce any answers to submit.")
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # 4. Prepare Submission 
    submission_data = {
        "username": username.strip(), 
        "agent_code": agent_code, 
        "answers": answers_payload
    }
    status_update = f"SmolAgent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    # 5. Submit
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        print("Submission successful.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except requests.exceptions.HTTPError as e:
        error_detail = f"Server responded with status {e.response.status_code}."
        try:
            error_json = e.response.json()
            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
        except requests.exceptions.JSONDecodeError:
            error_detail += f" Response: {e.response.text[:500]}"
        status_message = f"Submission Failed: {error_detail}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# 🤖 SmolAgent GAIA Evaluation Runner")
    gr.Markdown(
        """
        **Enhanced Agent for GAIA Dataset:**
        
        🛠️ **Tools Available:**
        - 🔍 **DuckDuckGoSearchTool**: Real-time web search capabilities
        - 🌐 **VisitWebpageTool**: Can visit and analyze web pages
        - 🧮 **Math Calculator**: Safe mathematical calculations
        - 📊 **Data Analysis**: Basic data analysis capabilities  
        - ✅ **Fact Checker**: Helps verify claims with authoritative sources
        - 🧠 **Advanced Reasoning**: Structured problem-solving approach
        
        🎯 **GAIA Format Compliance:**
        - Numbers without commas or units (unless specified)
        - Strings without articles or abbreviations
        - Proper comma-separated lists
        - Extracts only the final answer for submission

        **Instructions:**
        1. Log in to your Hugging Face account using the button below.
        2. Click 'Run Evaluation & Submit All Answers' to start the evaluation.
        3. The agent will process all questions using multiple tools and reasoning steps.

        ---
        **Note:** This agent follows GAIA's strict answer formatting requirements and uses advanced reasoning with multiple tools.
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers")

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " SmolAgent Starting " + "-"*30)
    # Check for SPACE_HOST and SPACE_ID at startup for information
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")  # Get SPACE_ID at startup

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup:  # Print repo URLs if SPACE_ID is found
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")

    print("-"*(60 + len(" SmolAgent Starting ")) + "\n")

    print("Launching Gradio Interface for SmolAgent GAIA Evaluation...")
    demo.launch(debug=True, share=False)