import os
import gradio as gr
import requests
import inspect
import pandas as pd
import time
import sys
from io import StringIO
from typing import TypedDict, Annotated
from langchain_core.tools import tool
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_community.tools import DuckDuckGoSearchRun
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import tools_condition, ToolNode
from langchain_core.messages import AnyMessage, HumanMessage, SystemMessage, ToolMessage
from langchain_groq import ChatGroq
# Detect if running locally or on Hugging Face Spaces
IS_LOCAL = os.getenv("SPACE_ID") is None

if IS_LOCAL:
    from langchain_ollama import ChatOllama
else:
    try:
        from langchain_ollama import ChatOllama
    except ImportError:
        from langchain_community.chat_models import ChatOllama

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

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
if IS_LOCAL:
    llm = ChatOllama(
        model="gemma4:31b", 
        temperature=0,
    )
else:
    # When on Hugging Face, use a remote endpoint wrapped in ChatHuggingFace for tool support
    llm_base = HuggingFaceEndpoint(
        repo_id = "Qwen/Qwen2.5-Coder-32B-Instruct",
        huggingfacehub_api_token=os.environ.get("HUGGINGFACEHUB_API_TOKEN")
    )
    llm = ChatHuggingFace(llm=llm_base)

system_prompt = """You are an AI assistant taking the GAIA benchmark. You must output ONLY the final answer. Do not include any explanations, conversational text or formatting. If the answer is a number, output just the number. If it is a list, output a comma-separated list."""

# Tool Definition
search_tool = DuckDuckGoSearchRun()

@tool
def python_repl(code:str) -> str:
    """
    Executes Python code and returns the standard output.
    Use this to read files (like CSVs or Excel), process data with pandas or do math.
    CRITICAL: You MUST use print() to output the final result so it can be captured.
    """

    old_stdout = sys.stdout
    redirected_output = sys.stdout = StringIO()

    try: 
        print(f"\n--- [Executing Python Code] ---\n{code}\n------------------------------")
        # Execute the code in the global namespace
        exec(code, globals())
        sys.stdout = old_stdout
        output = redirected_output.getvalue()
        print(f"--- [Code Output] ---\n{output}\n--------------------")
        return output if output else "Code executed successfully, but printed nothing. Use print() to see output."
    except Exception as e:
        sys.stdout = old_stdout
        return f"Error executing code: {e}"

# Tools Instantiation
tools = [search_tool, python_repl]
chat_with_tools = llm.bind_tools(tools)

class AgentState(TypedDict):
    messages:Annotated[list[AnyMessage], add_messages]

def assistant(state:AgentState):
    # Log the last tool result if it exists
    last_message = state["messages"][-1]
    if isinstance(last_message, ToolMessage):
        print(f"--- [Tool Result Received] ---\n{last_message.content}\n-----------------------------")

    print("\n--- [Assistant is thinking] ---")
    response = chat_with_tools.invoke(state["messages"])
    
    # Log content if it's not empty
    if response.content:
        print(f"Assistant Response: {response.content}")
    
    # Log tool calls
    if hasattr(response, 'tool_calls') and response.tool_calls:
        for tc in response.tool_calls:
            print(f"Tool Call: {tc['name']} with args: {tc['args']}")
            
    return {
        "messages": [response]
    }

# Graph Instantiation
builder = StateGraph(AgentState)

# Graph Nodes
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))

#Graph Edges
builder.add_edge(START, "assistant")
builder.add_conditional_edges(
    "assistant",
    tools_condition
)
builder.add_edge("tools", "assistant")

# Agent Compile
my_agent = builder.compile()

class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
    def __call__(self, question: str, file_name: str = None) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        
        # 1. Construct the prompt with the file path if it exists
        if file_name:
            # Note: You may need to adjust the path depending on where your space saves downloaded files.
            # Usually, the grading space provides the file name, and it sits in the same directory.
            prompt = f"Question: {question}\nAttached File: {file_name}\n\nUse the python_repl tool to read and analyze this file using pandas."
        else: 
            prompt = f"Question: {question}"

        # Inject the strict GAIA System Prompt and the Human Prompt
        messages = [
            SystemMessage(content=system_prompt),
            HumanMessage(content=question)
        ]

        # Invoke Agent
        response_state = my_agent.invoke({"messages": messages})

        # Final answer
        final_answer = response_state["messages"][-1].content
        print(f"--- [Final Answer for this Question] ---\n{final_answer}\n")
        return final_answer

def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    if IS_LOCAL:
        username = "local_user"
        print(f"Running in LOCAL MODE. Using default username: {username}")
    else:
        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
    if IS_LOCAL:
        space_id = "local-test-space"
        agent_code = "local-execution"
    else:
        space_id = os.getenv("SPACE_ID")
        agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    
    print(f"Agent code link: {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")
        file_name = item.get("file_name") 

        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue

        # Skip questions with files if running locally
        if IS_LOCAL and file_name:
            print(f"Skipping task {task_id} because it requires a file: {file_name}")
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": "SKIPPED (Local run - No files)"})
            continue

        try:
            submitted_answer = agent(question_text, file_name)
            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})
            
            # Add a 15-second pause between questions!
            print("Pausing for 15 seconds to respect Groq rate limits...")
            time.sleep(15)
            
        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


def run_single_test(question):
    """Runs the agent on a single question and returns the answer."""
    try:
        agent = BasicAgent()
        answer = agent(question)
        return answer
    except Exception as e:
        return f"Error running agent: {e}"

# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# GAIA Agent Runner & Evaluator")
    
    with gr.Tabs():
        with gr.TabItem("Single Question Test"):
            gr.Markdown("### Test your agent on a single question")
            test_input = gr.Textbox(
                label="Question", 
                placeholder="Enter a GAIA-style question here...", 
                lines=3,
                value="How many studio albums were published by Mercedes Sosa throughout her career?"
            )
            test_button = gr.Button("Run Agent Test")
            test_output = gr.Textbox(label="Agent Response", interactive=False, lines=5)
            
            test_button.click(
                fn=run_single_test,
                inputs=[test_input],
                outputs=[test_output]
            )

        with gr.TabItem("Full Evaluation & Submission"):
            gr.Markdown(
                """
                **Instructions:**
                1. Log in to your Hugging Face account below (required for submission).
                2. Click 'Run Evaluation' to fetch ALL questions from the benchmark, run your agent on them, and submit.
                
                *Note: This will skip questions requiring files in local mode.*
                """
            )

            if not IS_LOCAL:
                gr.LoginButton()

            run_button = gr.Button("Run Full 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 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)