import os
import gradio as gr
import requests
import inspect
import pandas as pd

from huggingface_hub import hf_hub_download, login

from smolagents import CodeAgent
from smolagents import OpenAIServerModel
from smolagents import Tool
from smolagents import PythonInterpreterTool
from smolagents import DuckDuckGoSearchTool

# (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 ------

# Global variables
HF_DATASET_TOKEN = os.getenv("HF_DATASET_TOKEN")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

# GAIA Dataset file dowloading
"""
    Basic function to download a GAIA dataset validation file
"""
def get_GAIA_dataset_validation_file(file_name: str):
    response = hf_hub_download(
        repo_id="gaia-benchmark/GAIA", 
        filename= f"2023/validation/{file_name}", 
        repo_type="dataset"
    )
    return response

"""
    Basic function to download a GAIA dataset test file
"""
def get_GAIA_dataset_test_file(file_name: str):
    response = hf_hub_download(
        repo_id="gaia-benchmark/GAIA", 
        filename= f"2023/test/{file_name}", 
        repo_type="dataset"
    )
    return response

"""
    Basic function to download a GAIA dataset file (validation attempted first)
"""
def get_GAIA_dataset_file(file_name: str):
    
    global HF_DATASET_TOKEN
    login(token = HF_DATASET_TOKEN)
    
    response = None
    try:
        response = get_GAIA_dataset_validation_file(file_name)
    except:
        response = get_GAIA_dataset_test_file(file_name)
            
    return response

class BasicAgent:
    def __init__(self):
        print("Starting the initialization of model.")
        
        global OPENAI_API_KEY
        
        model = OpenAIServerModel(
            model_id="gpt-4o-mini-2024-07-18",
            api_key = OPENAI_API_KEY 
        )
        print("Core model has been initialized.")
        
        self.tools = [
            DuckDuckGoSearchTool()
        ]
        print("Agent tools have been initialized.")

        self.agent = CodeAgent(
            model = model,
            tools = self.tools,
            add_base_tools=True  # Add basic tools like math
        )
        print("Core agent has been initialized.")

    def __call__(self, question: str) -> str:
        print("#"*20)
        print(f"ℹ️ Agent received question: {question}")
        print("#"*20)
        try:
            # send the question content to the agent
            answer = self.agent.run(question)
            print("#"*20)
            print(f"✅ Agent returning the answer: {answer}")
            print("#"*20)
            
            # return the answer
            return answer
        except Exception as e:
            print("!"*20)
            print(f"❗Error running agent {str(e)}")
            print("!"*20)

def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    print("!!!!!!!!!!!!! HANDLING DATASET FILE")
    response = get_GAIA_dataset_file("076c8171-9b3b-49b9-a477-244d2a532826.xlsx")
    print(response)

    return
    
    # --- 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...")
    question_index = 1
    for item in questions_data:
        print(f"ℹ️ Handling question #: {question_index}")
        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 = "Placeholder"
            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})
            print(f"✅ Successful handling of question #: {question_index}")
        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}"})

        question_index = question_index + 1

    # REMOVE: prevent payload submission!!!
    return
    
    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(
        """
        # HuggingFace agents course - final assignement implementation.
        An OPEN AI key will be needed to run this assignment.
        """
    )

    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)