import os
import gradio as gr
import requests
import pandas as pd
from langchain.agents import create_agent
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_openai import ChatOpenAI
from langchain.tools import tool
from dotenv import load_dotenv
from langchain_community.document_loaders import ArxivLoader, WikipediaLoader
from ddgs import DDGS

# Load environment variables
load_dotenv()

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

# --- Agent Setup ---
openai_key = os.getenv("OPENAI_API_KEY")
googleai_key = os.getenv("GOOGLE_API_KEY")

# Initialize the model
model = ChatGoogleGenerativeAI(
    model="gemini-2.5-flash",
    temperature=0,
    max_tokens=5000,
    timeout=None,
    max_retries=2,
)

# --- Tools Definition ---
@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers.
    Args:
        a: first int
        b: second int
    """
    return a * b

@tool
def add(a: int, b: int) -> int:
    """Add two numbers.
    
    Args:
        a: first int
        b: second int
    """
    return a + b

@tool
def subtract(a: int, b: int) -> int:
    """Subtract two numbers.
    
    Args:
        a: first int
        b: second int
    """
    return a - b

@tool
def divide(a: int, b: int) -> int:
    """Divide two numbers.
    
    Args:
        a: first int
        b: second int
    """
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int, b: int) -> int:
    """Get the modulus of two numbers.
    
    Args:
        a: first int
        b: second int
    """
    return a % b

@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a query and return maximum 2 results.
    
    Args:
        query: The search query."""
    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
            for doc in search_docs
        ])
    return {"wiki_results": formatted_search_docs}

@tool
def web_search(query: str) -> str:
    """Search DDGS for a query and return maximum 3 results.
    
    Args:
        query: The search query."""
    search_docs = DDGS().text(query,max_results=3)
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'Title:{doc["title"]}\nContent:{doc["body"]}\n--\n'
            for doc in search_docs
        ])
    return formatted_search_docs

@tool
def arvix_search(query: str) -> str:
    """Search Arxiv for a query and return maximum 3 result.
    
    Args:
        query: The search query."""
    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
            for doc in search_docs
        ])
    return {"arvix_results": formatted_search_docs}

@tool
def image_search(query: str) -> str:
    """Searches DDGS for an image query and returns maximum 10 image results"""
    search_images = DDGS().images(query=query)
    formatted_result = "\n\n---\n\n".join(
        [
            f'Image Title:{image["title"]}\nImage URL: {image["url"]}'
            for image in search_images
        ])


# Tools list
tools = [
    multiply, add, subtract, divide, modulus,
    wiki_search, web_search, arvix_search, image_search
]

# System prompt
sys_prompt = """You are a helpful agent, please provide clear and concise answers to asked questions.
Keep your word limit for answers as minimum as you can. You are equipped with the following tools:
1. [multiply], [add], [subtract], [divide], [modulus] - basic calculator operations.
2. [wiki_search] - search Wikipedia and return up to 2 documents as text.
3. [web_search] - perform a web search and return up to 3 documents as text.
4. [arxiv_search] - search arXiv and return up to 3 documents as text.
5. [image_search] - Searches the internet for an image query and returns maximum 10 image results

Under any circumstances, if you fail to provide the accurate answer expected by the user, you may say the same to the user and provide a similar answer which is approximately the closest. Disregard spelling mistakes and provide answer with results retreived from the correct spelling. 

For every tool you use, append a single line at the end of your response exactly in this format:
[TOOLS USED: (tool_name)]
When no tools are used, append:
[TOOLS USED WERE NONE]"""

# --- Agent Class ---
class GAIAAgent:
    def __init__(self):
        print("GAIAAgent initialized with LangChain agent.")
        try:
            self.agent = create_agent(model, tools=tools, system_prompt=sys_prompt)
            print("Agent created successfully.")
        except Exception as e:
            print(f"Error creating agent: {e}")
            raise
    
    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 100 chars): {question[:100]}...")
        try:
            result = self.agent.invoke({
                "messages": [{"role": "user", "content": question}]
            })
            
            # Get the content from the last message
            raw_content = result["messages"][-1].content
            
            # Parse the response format: list of dicts with 'text' key
            if isinstance(raw_content, list) and len(raw_content) > 0:
                if isinstance(raw_content[0], dict) and 'text' in raw_content[0]:
                    answer = raw_content[0]['text']
                else:
                    # Fallback: convert list to string
                    answer = str(raw_content)
            elif isinstance(raw_content, str):
                answer = raw_content
            else:
                answer = str(raw_content)
            
            print(f"Agent returning answer (first 100 chars): {answer[:100]}...")
            return answer
        except Exception as e:
            print(f"Error in agent execution: {e}")
            import traceback
            traceback.print_exc()
            return f"Error: {str(e)}"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the GAIAAgent 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"

    # 1. Instantiate Agent
    try:
        agent = GAIAAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    
    # In the case of an app running as a Hugging Face space, this link points toward your codebase
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Local"
    print(f"Agent code location: {agent_code}")

    # 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 agent on {len(questions_data)} questions...")
    for idx, item in enumerate(questions_data, 1):
        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 {idx}/{len(questions_data)} - Task ID: {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[:200] + "..." if len(submitted_answer) > 200 else submitted_answer
            })
        except Exception as e:
            print(f"Error running agent on task {task_id}: {e}")
            results_log.append({
                "Task ID": task_id,
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
                "Submitted Answer": f"AGENT ERROR: {e}"
            })

    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"Agent 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("# GAIA Benchmark Agent Evaluation")
    gr.Markdown(
        """
        **Instructions:**
        1. This app integrates a LangChain agent with multiple tools (calculator, Wikipedia, web search, Arxiv).
        2. Log in to your Hugging Face account using the button below.
        3. Click 'Run Evaluation & Submit All Answers' to fetch GAIA questions, run your agent, and submit answers.
        
        **Agent Tools:**
        - Mathematical operations (add, subtract, multiply, divide, modulus)
        - Wikipedia search
        - Web search (Tavily)
        - Arxiv academic paper search
        
        **Note:** Processing all questions may take several minutes depending on the number of questions and API response times.
        """
    )

    gr.LoginButton()

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

    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 + " App Starting " + "-"*30)
    
    # Check for required environment variables
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")
    google_api_key = os.getenv("GOOGLE_API_KEY")
    tavily_api_key = os.getenv("TAVILY_API_KEY")

    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(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?).")

    if google_api_key:
        print("✅ GOOGLE_API_KEY found")
    else:
        print("⚠️  GOOGLE_API_KEY not found - agent will not work without it!")

    if tavily_api_key:
        print("✅ TAVILY_API_KEY found")
    else:
        print("⚠️  TAVILY_API_KEY not found - web search will not work!")

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

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