import os
import gradio as gr
import requests
import json
import re
import numexpr
import pandas as pd
import time
import math
import pdfminer
from ctransformers import AutoModelForCausalLM
from duckduckgo_search import DDGS
from pdfminer.high_level import extract_text
from bs4 import BeautifulSoup
import html2text
from typing import Dict, Any, List, Tuple, Callable

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
MAX_STEPS = 6  # Limit reasoning steps for performance
MAX_TOKENS = 256  # Limit token generation
MODEL_NAME = "TheBloke/phi-3-mini-128k-instruct-GGUF"
MODEL_FILE = "phi-3-mini-128k-instruct.Q4_K_M.gguf"

# --- Load Quantized Model ---
print("Loading quantized model...")
start_time = time.time()
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    model_file=MODEL_FILE,
    model_type="phi3",
    gpu_layers=0,  # CPU only
    context_length=4096
)
load_time = time.time() - start_time
print(f"Model loaded in {load_time:.2f} seconds")

# --- Tools for GAIA Agent ---
def web_search(query: str) -> str:
    """Search the web using DuckDuckGo"""
    try:
        with DDGS() as ddgs:
            results = [r for r in ddgs.text(query, max_results=3)]
            return json.dumps(results)
    except Exception as e:
        return f"Search error: {str(e)}"

def calculator(expression: str) -> str:
    """Evaluate mathematical expressions safely"""
    try:
        return str(numexpr.evaluate(expression))
    except Exception as e:
        return f"Calculation error: {str(e)}"

def read_pdf(file_path: str) -> str:
    """Extract text from PDF files"""
    try:
        return extract_text(file_path)
    except Exception as e:
        return f"PDF read error: {str(e)}"

def read_webpage(url: str) -> str:
    """Fetch and extract text from web pages"""
    try:
        response = requests.get(url, timeout=10)
        soup = BeautifulSoup(response.text, 'html.parser')
        return soup.get_text(separator=' ', strip=True)[:2000]  # Limit text
    except Exception as e:
        return f"Webpage read error: {str(e)}"

TOOLS = {
    "web_search": web_search,
    "calculator": calculator,
    "read_pdf": read_pdf,
    "read_webpage": read_webpage
}

# --- GAIA Agent Implementation ---
class GAIA_Agent:
    def __init__(self):
        self.tools = TOOLS
        self.history = []
        self.system_prompt = (
            "You are an expert GAIA problem solver. Use these tools: {web_search, calculator, read_pdf, read_webpage}.\n"
            "Guidelines:\n"
            "1. Think step-by-step. Explain reasoning\n"
            "2. Use tools for calculations, searches, or file operations\n"
            "3. Tools must be called as: ```json\n{'tool': 'tool_name', 'args': {'arg1': value}}```\n"
            "4. Final Answer must be exact and standalone\n\n"
            "Example:\n"
            "Question: \"What's the population density of France? (File: france_data.pdf)\"\n"
            "Thought: Need population and area. Read PDF first.\n"
            "Action: ```json\n{'tool': 'read_pdf', 'args': {'file_path': 'france_data.pdf'}}```\n"
            "Observation: Population: 67.8M, Area: 643,801 km²\n"
            "Thought: Now calculate density: 67,800,000 / 643,801\n"
            "Action: ```json\n{'tool': 'calculator', 'args': {'expression': '67800000 / 643801'}}```\n"
            "Observation: 105.32\n"
            "Final Answer: 105.32 people/km²"
        )

    def __call__(self, question: str) -> str:
        print(f"\nProcessing: {question[:80]}...")
        self.history = [f"Question: {question}"]
        
        for step in range(MAX_STEPS):
            prompt = self._build_prompt()
            response = self._call_model(prompt)
            
            if "Final Answer" in response:
                answer = response.split("Final Answer:")[-1].strip()
                print(f"Final Answer: {answer}")
                return answer
                
            tool_call = self._parse_tool_call(response)
            if tool_call:
                tool_name, args = tool_call
                observation = self._use_tool(tool_name, args)
                self.history.append(f"Observation: {observation}")
            else:
                self.history.append(f"Thought: {response}")
        
        return "Agent couldn't find solution within step limit"

    def _build_prompt(self) -> str:
        prompt = f"<|system|>\n{self.system_prompt}<|end|>\n"
        prompt += "<|user|>\n" + "\n".join(self.history) + "<|end|>\n"
        prompt += "<|assistant|>"
        return prompt

    def _call_model(self, prompt: str) -> str:
        start_time = time.time()
        response = model(
            prompt,
            max_new_tokens=MAX_TOKENS,
            temperature=0.01,
            stop=["<|end|>", "Observation:", "```"]
        )
        gen_time = time.time() - start_time
        print(f"Generated {len(response)} tokens in {gen_time:.2f}s: {response[:60]}...")
        return response

    def _parse_tool_call(self, text: str) -> Tuple[str, Dict] or None:
        try:
            json_match = re.search(r'```json\s*({.*?})\s*```', text, re.DOTALL)
            if json_match:
                tool_call = json.loads(json_match.group(1))
                return tool_call["tool"], tool_call["args"]
        except Exception as e:
            print(f"Tool parse error: {str(e)}")
        return None

    def _use_tool(self, tool_name: str, args: Dict) -> str:
        if tool_name not in self.tools:
            return f"Error: Unknown tool {tool_name}"
        
        print(f"Using tool: {tool_name}({args})")
        try:
            start_time = time.time()
            result = self.tools[tool_name](**args)
            exec_time = time.time() - start_time
            print(f"Tool executed in {exec_time:.2f}s")
            return str(result)[:500]  # Truncate long outputs
        except Exception as e:
            return f"Tool error: {str(e)}"

# --- Evaluation Runner ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    # ... [Keep the original run_and_submit_all function structure] ...
    # Only change the agent initialization:
    try:
        agent = GAIA_Agent()  # Use our custom agent
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    # ... [rest of the function remains unchanged] ...

# --- Gradio Interface ---
with gr.Blocks() as demo:
    # ... [Keep the original Gradio interface] ...
    # Only add resource monitoring:
    gr.Markdown(f"**Resource Info:** Using {MODEL_FILE} | Max steps: {MAX_STEPS} | Max tokens: {MAX_TOKENS}")
    
    # Add a clear button for history
    clear_btn = gr.Button("Clear History")
    clear_btn.click(lambda: [None, None], outputs=[status_output, results_table])
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)
            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)