import os
import gradio as gr
import requests
import pandas as pd
from langchain.agents import AgentExecutor, create_react_agent
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.tools import tool
from langchain.prompts import PromptTemplate
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
#from langchain_openai import ChatOpenAI  # OpenAI-compatible for Groq API
from ddgs import DDGS  # Updated DuckDuckGo Search
from dotenv import load_dotenv
import re
import json

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Define Tools ---
@tool
def python_code_executor(code: str) -> str:
    """Execute Python code and return the result as a string. Use for calculations or data processing."""
    try:
        local_vars = {}
        exec(code, {}, local_vars)
        return str(local_vars.get("result", "No result defined. Set 'result' variable."))
    except Exception as e:
        return f"Error: {str(e)}"

@tool
def download_file(url: str) -> str:
    """Download a file from URL and return its content (text if possible)."""
    try:
        response = requests.get(url, timeout=10)
        response.raise_for_status()
        return response.text[:1000]  # Truncate for brevity
    except Exception as e:
        return f"Error downloading: {str(e)}"

@tool
def duckduckgo_search(query: str) -> str:
    """Perform a DuckDuckGo search and return top results as a short summary."""
    try:
        with DDGS() as ddgs:
            results = list(ddgs.text(query, max_results=3))
        if not results:
            return "No good results found."
        return json.dumps([{"title": r["title"], "snippet": r["body"]} for r in results])
    except Exception as e:
        return f"Search error: {str(e)}"

@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 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 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
        
# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
        #llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0)

        #tools = [duckduckgo_search, python_code_executor, download_file]
        tools = [duckduckgo_search, python_code_executor, download_file,arvix_search,wiki_search,multiply,add,subtract,divide]
        # Prompt for short, exact answers (GAIA-style) with required ReAct variables
        prompt_template = PromptTemplate.from_template("""
        You are an AI agent solving GAIA benchmark tasks. Use tools if needed (web search, code execution, file download).
        
        You have access to the following tools:
        {tools}
        
        Use the following format:
        
        Question: the input question you must answer
        Thought: you should always think about what to do
        Action: the action to take, should be one of [{tool_names}]
        Action Input: the input to the action
        Observation: the result of the action
        ... (this Thought/Action/Action Input/Observation can repeat N times)
        Thought: I now know the final answer
        Final Answer: the final answer to the original input question
        
        Begin!
        
        Question: {question}
        File content (if any): {file_content}
        
        Reason step-by-step, but return ONLY the final answer as a short string (e.g., a number or phrase). No explanations, no extra text.
        {agent_scratchpad}
        """)
        
        agent = create_react_agent(llm, tools, prompt_template)
        self.agent_executor = AgentExecutor(
            agent=agent,
            tools=tools,
            verbose=True,  # Set to False for production to reduce logs
            max_iterations=10  # Limit for efficiency
        )
        
    def __call__(self, question: str, task_id: str = None, api_url: str = DEFAULT_API_URL) -> str:
        print(f"Agent processing question: {question[:50]}...")
        
        # Fetch file if task involves one
        file_content = ""
        if task_id:
            file_url = f"{api_url}/files/{task_id}"
            try:
                response = requests.get(file_url, timeout=10)
                file_content = response.text[:1000] if response.ok else ""
            except Exception as e:
                print(f"File fetch error for {task_id}: {e}")
                pass
        
        try:
            response = self.agent_executor.invoke({"question": question, "file_content": file_content})
            # Parse to ensure shortness
            short_answer = re.search(r'(?s)(.*)', response["output"]).group(1).strip()
            if len(short_answer) > 50:  # Enforce brevity
                short_answer = short_answer[:50] + "... (truncated)"
            print(f"Agent returning short answer: {short_answer}")
            return short_answer
        except Exception as e:
            print(f"Agent error: {e}")
            return "Error"

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"

    # 1. Instantiate Agent ( modify this part to create your agent)
    try:
        agent = BasicAgent()
    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 ( usefull for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(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 item in 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
        try:
            #submitted_answer = agent(question_text)
            submitted_answer = agent(question_text, task_id=task_id)
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": 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, "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("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**

        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.

        ---
        **Disclaimers:**
        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
        """
    )

    gr.LoginButton()

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

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    # Removed max_rows=10 from DataFrame constructor
    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 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(" App Starting ")) + "\n")

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