import os
import gradio as gr
import requests
import inspect
import pandas as pd
import asyncio
from smolagents import ToolCallingAgent, InferenceClientModel, OpenAIServerModel
from smolagents import DuckDuckGoSearchTool, Tool, CodeAgent
from huggingface_hub import login
#h
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

#login(token=os.environ["HUGGINGFACEHUB_API_TOKEN"])
openai_key = os.environ.get("OPENAI_API_KEY")

search_tool = DuckDuckGoSearchTool()

##Tool 2 
from smolagents import Tool
from huggingface_hub import hf_hub_download
import pandas as pd

class ExcelAnalysisTool(Tool):
    name = "excel_analysis"
    description = (
        "Loads an Excel file from the GAIA dataset on Hugging Face and calculates "
        "the total sales for items labeled as 'food', excluding drinks. "
        "Provide input as a string with the filename, e.g., 'sales_data.xlsx'."
    )

    inputs = {
        "filename": {
            "type": "string",
            "description": "The name of the Excel file (e.g., 'sales_data.xlsx')"
        }
    }

    output_type = "string"
    repo_id = "gaia-benchmark/GAIA"

    def forward(self, filename: str) -> str:
        try:
            file_path = hf_hub_download(
                repo_id=self.repo_id,
                filename=filename,
                repo_type="dataset"
            )
            df = pd.read_excel(file_path)
            food_sales = df[
                (df['category'].str.lower() == 'food') &
                (df['item'].str.lower() != 'drinks')
            ]
            total_sales = food_sales['sales'].sum()
            return f"Total sales for food items: ${total_sales:.2f}"
        except FileNotFoundError:
            return "Error: The specified file was not found."
        except KeyError as e:
            return f"Error: Missing expected column in the Excel file: {str(e)}"
        except Exception as e:
            return f"An unexpected error occurred: {str(e)}"

##Tool 3
import wikipedia
from smolagents import Tool

class WikiTool(Tool):
    name = "wiki_tool"
    description = (
        "Performs Wikipedia lookups. Actions supported: 'summary' and 'is_historical_country'."
    )

    inputs = {
        "action": {
            "type": "string",
            "description": "The action to perform: 'summary' or 'is_historical_country'"
        },
        "topic": {
            "type": "string",
            "description": "The topic or country name to look up"
        }
    }

    output_type = "string"

    def forward(self, action: str, topic: str) -> str:
        if action == "summary":
            return self.fetch_summary(topic)
        elif action == "is_historical_country":
            return self.is_historical_country(topic)
        else:
            return "Error: Unknown action. Use 'summary' or 'is_historical_country'."

    def fetch_summary(self, topic: str) -> str:
        try:
            return wikipedia.summary(topic, sentences=3)
        except wikipedia.DisambiguationError as e:
            return f"Disambiguation: {e.options[:5]}"
        except wikipedia.PageError:
            return "No page found."
        except Exception as e:
            return f"Unexpected error: {str(e)}"

    def is_historical_country(self, topic: str) -> str:
        try:
            summary = wikipedia.summary(topic, sentences=2).lower()
            keywords = [
                "former country", "no longer exists", "historical country",
                "was a country", "defunct", "dissolved", "existed until",
                "disestablished", "merged into"
            ]
            return "yes" if any(k in summary for k in keywords) else "no"
        except:
            return "no"

wiki_tool = WikiTool()
excel_tool = ExcelAnalysisTool()

async def run_and_submit_all(profile: gr.OAuthProfile | None):
    log_output = ""

    try:
        
        agent = ToolCallingAgent(
            tools=[search_tool, wiki_tool, excel_tool],
                        model=OpenAIServerModel(
                    model_id="gpt-4o",            # ✅ valid OpenAI model name
                    api_key=os.environ["OPENAI_API_KEY"] # ✅ securely load from environment
                ),
            max_steps=20,
            verbosity_level=2
        )
    except Exception as e:
        yield f"Error initializing agent: {e}", None, log_output
        return

    space_id = os.getenv("SPACE_ID")
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    questions_url = f"{DEFAULT_API_URL}/questions"
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            yield "Fetched questions list is empty or invalid format.", None, log_output
            return
    except Exception as e:
        yield f"Error fetching questions: {e}", None, log_output
        return

    results_log = []
    answers_payload = []
    loop = asyncio.get_event_loop()

    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:
            continue

        log_output += f"🔍 Solving Task ID: {task_id}...\n"
        yield None, None, log_output

        try:
            system_prompt = (
                """You must only reply with a single line:
                    FINAL ANSWER: [your answer]
                    
                    Never include reasoning, markdown, Task Outcome, Explanation, or examples.
                    NEVER use numbered points or extra formatting.
                    
                    If your answer is a string, write it in lowercase, no articles, no quotes.
                    If your answer is a number, use digits only. If the answer is "no one" or "none", write exactly that.
                    
                    DO NOT provide any explanation or context. Just the line: FINAL ANSWER: ...
                    """
            )
            full_prompt = system_prompt + f"Question: {question_text.strip()}"

            agent_result = await loop.run_in_executor(None, agent, full_prompt)

            # Extract final answer cleanly
            if isinstance(agent_result, dict) and "final_answer" in agent_result:
                final_answer = str(agent_result["final_answer"]).strip()
            elif isinstance(agent_result, str):
                response_text = agent_result.strip()

                # Remove known boilerplate
                if "Here is the final answer from your managed agent" in response_text:
                    response_text = response_text.split(":", 1)[-1].strip()

                if "FINAL ANSWER:" in response_text:
                    _, final_answer = response_text.rsplit("FINAL ANSWER:", 1)
                    final_answer = final_answer.strip()
                else:
                    final_answer = response_text
            else:
                final_answer = str(agent_result).strip()

            answers_payload.append({
                "task_id": task_id,
                "submitted_answer": final_answer
            })

            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": final_answer
            })

            log_output += f"✅ Done: {task_id} — Answer: {final_answer[:60]}\n"
            yield None, None, log_output

        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}"
            })
            log_output += f"⛔️ Error: {task_id} — {e}\n"
            yield None, None, log_output

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

    username = profile.username if profile else "unknown"
    submit_url = f"{DEFAULT_API_URL}/submit"
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    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.')}"
        )
        results_df = pd.DataFrame(results_log)
        yield final_status, results_df, log_output
    except Exception as e:
        status_message = f"Submission Failed: {e}"
        results_df = pd.DataFrame(results_log)
        yield status_message, results_df, log_output

with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown("""
    **Instructions:**
    1. Clone this space and define your agent logic.
    2. Log in to your Hugging Face account.
    3. Click 'Run Evaluation & Submit All Answers'.
    ---
    **Note:**
    The run may take time. Async is now used to improve responsiveness.
    """)

    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)
    progress_log = gr.Textbox(label="Progress Log", lines=10, interactive=False)

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

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    if space_host_startup:
        print(f"✅ SPACE_HOST: https://{space_host_startup}.hf.space")
    if space_id_startup:
        print(f"✅ SPACE_ID: https://huggingface.co/spaces/{space_id_startup}")

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