import os
import gradio as gr
import requests
import json
import re
import numexpr
import pandas as pd
import math
import pdfminer
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, Optional
from dotenv import load_dotenv
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch
import time
import gc

# --- Load Environment Variables ---
load_dotenv()
SERPER_API_KEY = os.getenv("SERPER_API_KEY")

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
MAX_STEPS = 6
MAX_TOKENS = 256
MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct"

# --- Configure Environment for Hugging Face Spaces ---
os.environ["PIP_BREAK_SYSTEM_PACKAGES"] = "1"
os.environ["HF_HUB_DISABLE_SYMLINKS_WARNING"] = "1"
os.environ["BITSANDBYTES_NOWELCOME"] = "1"

print("Loading model (CPU-compatible)...")
start_time = time.time()

# Load model with explicit configuration for better compatibility
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    trust_remote_code=True,
    torch_dtype=torch.float32,  # Use float32 for CPU compatibility
    device_map="cpu",  # Explicitly set to CPU
    low_cpu_mem_usage=True,  # Optimize for low memory usage
    use_cache=False  # Disable cache to avoid DynamicCache issues
)

tokenizer = AutoTokenizer.from_pretrained(
    MODEL_NAME, 
    use_fast=False,
    trust_remote_code=True
)

# Ensure pad token is set
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

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 or Serper API"""
    try:
        if SERPER_API_KEY:
            # Use Serper API if key is available
            params = {
                'q': query,
                'num': 3,
                'hl': 'en',
                'gl': 'us'
            }
            headers = {
                'X-API-KEY': SERPER_API_KEY,
                'Content-Type': 'application/json'
            }
            response = requests.post(
                'https://google.serper.dev/search',
                headers=headers,
                json=params,
                timeout=10
            )
            results = response.json()
            if 'organic' in results:
                return json.dumps([r['title'] + ": " + r['snippet'] for r in results['organic'][:3]])
            return "No results found"
        else:
            # Fallback to DuckDuckGo
            with DDGS() as ddgs:
                results = [r for r in ddgs.text(query, max_results=3)]
                return json.dumps([r['title'] + ": " + r['body'] for r in results])
    except Exception as e:
        return f"Search error: {str(e)}"

def calculator(expression: str) -> str:
    """Evaluate mathematical expressions safely"""
    try:
        # Clean the expression
        expression = re.sub(r'[^\d+\-*/().\s]', '', expression)
        result = numexpr.evaluate(expression)
        return str(result)
    except Exception as e:
        return f"Calculation error: {str(e)}"

def read_pdf(file_path: str) -> str:
    """Extract text from PDF files"""
    try:
        text = extract_text(file_path)
        return text[:2000] if text else "No text found in PDF"
    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:
        headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
        }
        response = requests.get(url, timeout=10, headers=headers)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        
        # Remove script and style elements
        for script in soup(["script", "style"]):
            script.decompose()
            
        text = soup.get_text(separator=' ', strip=True)
        return text[:2000] if text else "No text found on webpage"
    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}"]
        
        try:
            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}")
                    
                # Clean up memory after each step
                if step % 2 == 0:
                    gc.collect()
            
            return "Agent couldn't find solution within step limit"
            
        except Exception as e:
            print(f"Error in agent execution: {str(e)}")
            return f"Agent error: {str(e)}"

    def _build_prompt(self) -> str:
        prompt = "<|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()
        
        try:
            # Tokenize input
            inputs = tokenizer(
                prompt, 
                return_tensors="pt", 
                return_attention_mask=True,
                truncation=True,
                max_length=3072  # Leave room for generation
            )
            
            # Move to same device as model
            inputs = {k: v.to(model.device) for k, v in inputs.items()}
            
            # Create generation config
            generation_config = GenerationConfig(
                max_new_tokens=MAX_TOKENS,
                temperature=0.01,
                do_sample=True,
                pad_token_id=tokenizer.pad_token_id,
                eos_token_id=tokenizer.eos_token_id,
                use_cache=False  # Disable cache to avoid DynamicCache issues
            )
            
            # Generate response
            with torch.no_grad():
                outputs = model.generate(
                    **inputs,
                    generation_config=generation_config
                )
            
            # Decode response
            full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
            response = full_response.split("<|assistant|>")[-1].strip()
            
            gen_time = time.time() - start_time
            print(f"Generated {len(response)} tokens in {gen_time:.2f}s: {response[:60]}...")
            
            # Clean up
            del inputs, outputs
            gc.collect()
            
            return response
            
        except Exception as e:
            print(f"Model generation error: {str(e)}")
            return f"Generation error: {str(e)}"

    def _parse_tool_call(self, text: str) -> Optional[Tuple[str, Dict]]:
        try:
            json_match = re.search(r'```json\s*({.*?})\s*```', text, re.DOTALL)
            if json_match:
                tool_call = json.loads(json_match.group(1))
                if "tool" in tool_call and "args" in tool_call:
                    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):
    """Fetches questions, runs agent, submits answers, and displays results"""
    space_id = os.getenv("SPACE_ID")
    
    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"

    try:
        agent = GAIA_Agent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(f"Agent code URL: {agent_code}")

    # Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=30)
        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 Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # Run Agent
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")
    
    for i, item in enumerate(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:
            print(f"Processing question {i+1}/{len(questions_data)}")
            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[:100] + "..." if len(question_text) > 100 else question_text, 
                "Submitted Answer": submitted_answer
            })
            
            # Clean up memory periodically
            if i % 5 == 0:
                gc.collect()
                
        except Exception as e:
            print(f"Error running agent on task {task_id}: {e}")
            error_answer = f"AGENT ERROR: {str(e)}"
            answers_payload.append({"task_id": task_id, "submitted_answer": error_answer})
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text, 
                "Submitted Answer": error_answer
            })

    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)

    # 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)

    # Submit
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=120)
        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 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

# --- Gradio Interface ---
with gr.Blocks(title="GAIA Agent Evaluation") as demo:
    gr.Markdown("# GAIA Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Log in to your Hugging Face account using the button below
        2. Click 'Run Evaluation & Submit All Answers' to start the evaluation
        3. View results and score in the output sections
        
        **Agent Information:**
        - Model: Phi-3-mini-4k-instruct (CPU optimized)
        - Tools: Web Search, Calculator, PDF Reader, Webpage Reader
        - Max Steps: 6 per question
        - Memory: Optimized for 2vCPU/16GB environment
        """
    )

    with gr.Row():
        gr.LoginButton()
    
    with gr.Row():
        run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary", size="lg")
    
    with gr.Row():
        status_output = gr.Textbox(
            label="Evaluation Status & Submission Result", 
            lines=5, 
            interactive=False,
            placeholder="Click the button above to start evaluation..."
        )
    
    with gr.Row():
        results_table = gr.DataFrame(
            label="Questions and Agent Answers", 
            wrap=True,
            interactive=False
        )
    
    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table],
        show_progress=True
    )

if __name__ == "__main__":
    print("\n" + "="*50)
    print("GAIA Agent Evaluation System Starting")
    print("="*50)
    
    space_host = os.getenv("SPACE_HOST")
    space_id = os.getenv("SPACE_ID")
    
    if space_host:
        print(f"✅ SPACE_HOST found: {space_host}")
    else:
        print("⚠️  SPACE_HOST not found")
        
    if space_id:
        print(f"✅ SPACE_ID found: {space_id}")
    else:
        print("⚠️  SPACE_ID not found")
    
    print("="*50)
    print("Launching Gradio Interface...")
    demo.launch(
        debug=False,  # Disable debug in production
        share=False,
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True
    )