app.py

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
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed

# --- 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 = 4  # Reduced from 6
MAX_TOKENS = 128  # Reduced from 256
MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct"
TIMEOUT_PER_QUESTION = 30  # 30 seconds max per question

# --- 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-optimized)...")
start_time = time.time()

# Load model with aggressive optimization
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    trust_remote_code=True,
    torch_dtype=torch.float32,
    device_map="cpu",
    low_cpu_mem_usage=True,
    use_cache=False,
    attn_implementation="eager"  # Use eager attention for better CPU performance
)

tokenizer = AutoTokenizer.from_pretrained(
    MODEL_NAME, 
    use_fast=True,  # Changed to True for faster tokenization
    trust_remote_code=True
)

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")

# --- Optimized Tools ---
def web_search(query: str) -> str:
    """Search the web with timeout and result limiting"""
    try:
        if SERPER_API_KEY:
            params = {'q': query, 'num': 2, '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=5  # Reduced timeout
            )
            results = response.json()
            if 'organic' in results:
                return json.dumps([f"{r['title']}: {r['snippet'][:100]}" for r in results['organic'][:2]])
            return "No results found"
        else:
            with DDGS() as ddgs:
                results = [r for r in ddgs.text(query, max_results=2)]
                return json.dumps([f"{r['title']}: {r['body'][:100]}" for r in results])
    except Exception as e:
        return f"Search error: {str(e)}"

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

def read_pdf(file_path: str) -> str:
    """Extract text from PDF with length limit"""
    try:
        text = extract_text(file_path)
        return text[:1000] if text else "No text found in PDF"  # Reduced limit
    except Exception as e:
        return f"PDF read error: {str(e)}"

def read_webpage(url: str) -> str:
    """Fast webpage reading with aggressive limits"""
    try:
        headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'}
        response = requests.get(url, timeout=5, headers=headers)  # Reduced timeout
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        
        for script in soup(["script", "style"]):
            script.decompose()
            
        text = soup.get_text(separator=' ', strip=True)
        return text[:1000] if text else "No text found on webpage"  # Reduced limit
    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
}

# --- Optimized GAIA Agent ---
class GAIA_Agent:
    def __init__(self):
        self.tools = TOOLS
        self.system_prompt = (
            "You are a GAIA problem solver. Tools: {web_search, calculator, read_pdf, read_webpage}.\n"
            "Be concise and direct. Use tools efficiently.\n"
            "Tool format: ```json\n{'tool': 'tool_name', 'args': {'arg1': value}}```\n"
            "End with: Final Answer: [exact answer]"
        )

    def __call__(self, question: str) -> str:
        start_time = time.time()
        print(f"Processing: {question[:50]}...")
        
        try:
            history = [f"Question: {question}"]
            
            for step in range(MAX_STEPS):
                # Check timeout
                if time.time() - start_time > TIMEOUT_PER_QUESTION:
                    return "TIMEOUT: Question took too long"
                
                prompt = self._build_prompt(history)
                response = self._call_model(prompt)
                
                if "Final Answer" in response:
                    answer = response.split("Final Answer:")[-1].strip()
                    elapsed = time.time() - start_time
                    print(f"Completed in {elapsed:.1f}s: {answer[:30]}...")
                    return answer
                    
                tool_call = self._parse_tool_call(response)
                if tool_call:
                    tool_name, args = tool_call
                    observation = self._use_tool(tool_name, args)
                    history.append(f"Action: {tool_name}")
                    history.append(f"Result: {observation}")
                else:
                    history.append(f"Thought: {response}")
                
                # Aggressive memory cleanup
                gc.collect()
            
            return "Could not solve within step limit"
            
        except Exception as e:
            print(f"Agent error: {str(e)}")
            return f"Error: {str(e)}"

    def _build_prompt(self, history: List[str]) -> str:
        prompt = "<|system|>\n" + self.system_prompt + "<|end|>\n"
        prompt += "<|user|>\n" + "\n".join(history) + "<|end|>\n"
        prompt += "<|assistant|>"
        return prompt

    def _call_model(self, prompt: str) -> str:
        try:
            inputs = tokenizer(
                prompt, 
                return_tensors="pt", 
                truncation=True,
                max_length=2048,  # Reduced context
                padding=False
            )
            
            generation_config = GenerationConfig(
                max_new_tokens=MAX_TOKENS,
                temperature=0.1,  # Less randomness for faster convergence
                do_sample=True,
                pad_token_id=tokenizer.pad_token_id,
                eos_token_id=tokenizer.eos_token_id,
                use_cache=False
            )
            
            with torch.no_grad():
                outputs = model.generate(
                    inputs.input_ids,
                    generation_config=generation_config,
                    attention_mask=inputs.attention_mask
                )
            
            full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
            response = full_response.split("<|assistant|>")[-1].strip()
            
            # Immediate cleanup
            del inputs, outputs
            torch.cuda.empty_cache() if torch.cuda.is_available() else None
            
            return response
            
        except Exception as 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:
            pass
        return None

    def _use_tool(self, tool_name: str, args: Dict) -> str:
        if tool_name not in self.tools:
            return f"Unknown tool: {tool_name}"
        
        try:
            result = self.tools[tool_name](**args)
            return str(result)[:300]  # Truncate results
        except Exception as e:
            return f"Tool error: {str(e)}"

# --- Optimized Evaluation Runner ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    """Fast evaluation with parallel processing where possible"""
    space_id = os.getenv("SPACE_ID")
    
    if not profile:
        return "Please Login to Hugging Face with the button.", None

    username = profile.username
    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:
        return f"Error initializing agent: {e}", None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    # Fetch Questions
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            return "No questions found.", None
        print(f"Processing {len(questions_data)} questions...")
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # Process questions with progress tracking
    results_log = []
    answers_payload = []
    total_start = time.time()
    
    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:
            continue
            
        try:
            print(f"[{i+1}/{len(questions_data)}] Processing {task_id}...")
            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[:80] + "..." if len(question_text) > 80 else question_text, 
                "Answer": submitted_answer[:100] + "..." if len(submitted_answer) > 100 else submitted_answer
            })
            
            # Memory cleanup every few questions
            if i % 3 == 0:
                gc.collect()
                
        except Exception as e:
            error_answer = f"ERROR: {str(e)}"
            answers_payload.append({"task_id": task_id, "submitted_answer": error_answer})
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text[:80] + "..." if len(question_text) > 80 else question_text, 
                "Answer": error_answer
            })

    total_time = time.time() - total_start
    print(f"All questions processed in {total_time:.1f} seconds")

    if not answers_payload:
        return "No answers generated.", pd.DataFrame(results_log)

    # Submit results
    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"Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
            f"Processing Time: {total_time:.1f}s\n"
            f"Message: {result_data.get('message', 'No message')}"
        )
        
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
        
    except Exception as e:
        error_msg = f"❌ Submission Failed: {str(e)}"
        results_df = pd.DataFrame(results_log)
        return error_msg, results_df

# --- Gradio Interface ---
with gr.Blocks(title="GAIA Agent - Fast Mode") as demo:
    gr.Markdown("# 🚀 GAIA Agent Evaluation (Optimized)")
    gr.Markdown(
        """
        **Fast Mode Optimizations:**
        - Reduced max steps: 4 per question
        - Shorter token generation: 128 tokens max
        - 30s timeout per question
        - Aggressive memory management
        
        **Usage:** Login → Click Run → View Results
        """
    )

    with gr.Row():
        gr.LoginButton()
    
    with gr.Row():
        run_button = gr.Button("🏃‍♂️ Run Fast Evaluation", variant="primary", size="lg")
    
    with gr.Row():
        status_output = gr.Textbox(
            label="📊 Status & Results", 
            lines=6, 
            interactive=False,
            placeholder="Ready to run evaluation..."
        )
    
    with gr.Row():
        results_table = gr.DataFrame(
            label="📝 Questions & 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("🚀 GAIA Agent Fast Mode Starting...")
    print(f"⚙️  Max Steps: {MAX_STEPS}, Max Tokens: {MAX_TOKENS}")
    print(f"⏱️  Timeout per question: {TIMEOUT_PER_QUESTION}s")
    
    demo.launch(
        debug=False,
        share=False,
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True
    )