app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import json
import re
import time
from typing import List, Dict, Any, Optional, Union, Tuple

# --- Import necessary libraries ---
from smolagents import CodeAgent
from smolagents.models import LiteLLMModel
from llama_index.core.tools import FunctionTool
from langgraph.graph import StateGraph, END

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

class GAIAToolkit:
    """Collection of tools for the GAIA benchmark"""
    
    @staticmethod
    def calculator(expression: str) -> str:
        """Calculate mathematical expressions
        
        Args:
            expression: Mathematical expression to evaluate
        
        Returns:
            Calculation result
        """
        try:
            # Secure evaluation of expression
            allowed_chars = set("0123456789+-*/().% ")
            if any(c not in allowed_chars for c in expression):
                return "Error: Expression contains invalid characters."
            
            result = eval(expression)
            return str(result)
        except Exception as e:
            return f"Error: {str(e)}"
    
    @staticmethod
    def search_web(query: str) -> str:
        """Search for information related to the query
        
        Args:
            query: Search query
        
        Returns:
            Search results as a string
        """
        # Mock search function (in a real implementation, this would use a search API)
        common_topics = {
            "population": "The most recent census data shows a population of 3,142,000 for the region.",
            "weather": "The current weather is sunny with a temperature of 22°C.",
            "capital": "The capital city is Springfield, established in 1822.",
            "economic": "The GDP growth rate is 3.2% year-over-year.",
            "science": "Recent advancements have led to a 40% improvement in efficiency.",
            "technology": "The latest version was released in March with 15 new features."
        }
        
        # Find the most relevant topic
        best_match = None
        best_score = 0
        for topic, info in common_topics.items():
            if topic.lower() in query.lower():
                if len(topic) > best_score:
                    best_score = len(topic)
                    best_match = info
        
        if best_match:
            return best_match
        
        # If no match found, return a generic response
        return f"Found information about '{query}': The data shows a significant trend with key values of 42, 73, and 128."
    
    @staticmethod
    def file_reader(file_id: str) -> str:
        """Read file content from the API
        
        Args:
            file_id: File ID
        
        Returns:
            File content
        """
        # In a real implementation, this would fetch files from the GAIA API
        # Here we simulate some common file contents
        file_contents = {
            "data1.csv": "id,name,value\n1,Alpha,42\n2,Beta,73\n3,Gamma,91\n4,Delta,27\n5,Epsilon,68",
            "text1.txt": "This is a sample text file.\nIt contains multiple lines.\nThe answer to the question is 42.\nThere are 5 total items in the inventory.",
            "data2.json": '{"data": [{"id": 1, "name": "Item1", "value": 42}, {"id": 2, "name": "Item2", "value": 73}]}'
        }
        
        # Try to match file based on ID
        for filename, content in file_contents.items():
            if file_id.lower() in filename.lower():
                return content
        
        # Default to a simple dataset
        return "id,name,value\n1,A,42\n2,B,73\n3,C,91"
    
    @staticmethod
    def analyze_text(text: str) -> Dict[str, Any]:
        """Analyze text to extract key information
        
        Args:
            text: Text to analyze
        
        Returns:
            Dictionary with analysis results
        """
        word_count = len(text.split())
        sentences = text.split('.')
        sentence_count = len([s for s in sentences if s.strip()])
        
        # Extract numbers from text
        numbers = re.findall(r'\d+', text)
        numbers = [int(n) for n in numbers]
        
        # Basic statistics
        stats = {
            "word_count": word_count,
            "sentence_count": sentence_count,
            "numbers": numbers
        }
        
        # If there are numbers, add some statistics
        if numbers:
            stats["sum"] = sum(numbers)
            stats["average"] = sum(numbers) / len(numbers)
            stats["min"] = min(numbers)
            stats["max"] = max(numbers)
        
        # Check for CSV format
        if ',' in text and '\n' in text:
            lines = text.strip().split('\n')
            if all(line.count(',') == lines[0].count(',') for line in lines[1:]):
                # Likely a CSV file
                headers = lines[0].split(',')
                data = []
                for line in lines[1:]:
                    if line.strip():
                        values = line.split(',')
                        row = {headers[i]: values[i] for i in range(min(len(headers), len(values)))}
                        data.append(row)
                stats["csv_data"] = data
                stats["csv_headers"] = headers
        
        # Check for JSON format
        if text.strip().startswith('{') and text.strip().endswith('}'):
            try:
                json_data = json.loads(text)
                stats["json_data"] = json_data
            except:
                pass
        
        return stats
    
    @staticmethod
    def extract_answer(reasoning: str) -> str:
        """Extract the final answer from reasoning text
        
        Args:
            reasoning: Text containing reasoning process
        
        Returns:
            Extracted answer
        """
        # Look for common answer identification patterns
        patterns = [
            r'(?:final answer|answer|result)(?:\s*:|\s+is)\s*([^.\n]+)',
            r'(?:the|my)\s+(?:final answer|answer|result)(?:\s+is|\s*:\s*)\s*([^.\n]+)',
            r'(?:conclude|determine|find)(?:\s+that)?\s+(?:the answer|the result|result|answer)(?:\s+is)?\s*:?\s*([^.\n]+)',
            r'([^.\n]+)(?:\s+is|\s*:\s*)(?:\s*the)?\s*(?:final answer|answer|result)'
        ]
        
        for pattern in patterns:
            matches = re.findall(pattern, reasoning, re.IGNORECASE)
            if matches:
                return matches[0].strip()
        
        # Fallback strategy: Look for numbers as potential answers
        numbers = re.findall(r'\b\d+(?:\.\d+)?\b', reasoning)
        if numbers:
            # Often the answer is the last mentioned number
            return numbers[-1]
        
        # If no clear answer format can be identified, split and return the last non-empty line
        lines = [line.strip() for line in reasoning.split('\n') if line.strip()]
        if lines:
            return lines[-1]
        
        return reasoning.strip()

class GAIAAgent:
    """
    Integrated agent for GAIA benchmark, combining the best features of smolagents, llamaindex, and langgraph
    """
    def __init__(self, api_key: Optional[str] = None):
        """Initialize the agent and its components"""
        print("Initializing GAIA Agent...")
        
        self.file_cache = {}  # For caching file contents
        self.setup_model(api_key)
        self.setup_tools()
        
        # Create custom prompt template based on our system prompt
        self.custom_prompt = self.create_system_prompt()
        
        # Create code execution agent (based on smolagents)
        self.code_agent = CodeAgent(
            model=self.model,
            tools=self.tools,
            verbosity_level=1  # 0=quiet, 1=normal, 2=verbose
        )
        
        # Modify the agent's prompt templates to include our custom prompt
        # This is how smolagents handles custom system prompts
        if hasattr(self.code_agent, 'prompt_templates') and 'system_prompt' in self.code_agent.prompt_templates:
            original_prompt = self.code_agent.prompt_templates['system_prompt']
            self.code_agent.prompt_templates['system_prompt'] = original_prompt + "\n\n" + self.custom_prompt
        
        # Set up state machine workflow (inspired by langgraph)
        self.setup_workflow()
        
        print("GAIA Agent initialized successfully")
    
    def setup_model(self, api_key: Optional[str]):
        """Set up the language model to use"""
        try:
            if api_key:
                # Use model with API key
                self.model = LiteLLMModel(
                    model_id="gpt-4o",  # or "anthropic/claude-3-5-sonnet-latest"
                    api_key=api_key,
                    temperature=0.1
                )
            else:
                # Use a free model
                self.model = LiteLLMModel(
                    model_id="deepseek-ai/deepseek-r1",  # or another free model
                    provider="together",  
                    temperature=0.1
                )
            print(f"Successfully set up model: {self.model}")
        except Exception as e:
            print(f"Error setting up model: {e}")
            # Use a simple fallback model
            self.model = LiteLLMModel(
                model_id="google/gemma-7b",
                provider="huggingface",
                temperature=0.1
            )
    
    def setup_tools(self):
        """Set up tools for the agent"""
        # Use FunctionTool interface from llama_index but integrate with smolagents
        self.tools = [
            FunctionTool.from_defaults(
                name="calculator",
                description="Calculate mathematical expressions like '2 + 2' or '(15 * 3) / 2'",
                fn=GAIAToolkit.calculator
            ),
            FunctionTool.from_defaults(
                name="search_web",
                description="Search for information related to a query",
                fn=GAIAToolkit.search_web
            ),
            FunctionTool.from_defaults(
                name="file_reader",
                description="Read file content given a file ID",
                fn=GAIAToolkit.file_reader
            ),
            FunctionTool.from_defaults(
                name="analyze_text",
                description="Analyze text to extract statistics and key information",
                fn=GAIAToolkit.analyze_text
            ),
            FunctionTool.from_defaults(
                name="extract_answer",
                description="Extract the final answer from reasoning",
                fn=GAIAToolkit.extract_answer
            )
        ]
    
    def create_system_prompt(self) -> str:
        """Create system prompt to guide agent behavior"""
        return """You are an expert AI assistant designed for the GAIA benchmark. The GAIA test evaluates AI systems' ability to solve multi-step problems.
                    Follow these guidelines:
                    1. Carefully analyze the question to determine required tools and solution steps.
                    2. Use the provided tools to perform calculations, search for information, and analyze text.
                    3. Keep reasoning clear and concise, focusing on solving the problem.
                    4. Final answers must be accurate and match the correct answer EXACTLY (exact match).
                    5. For numerical answers, return only the number (no units or explanation).
                    6. For text answers, ensure exact matching of the correct words.
                    IMPORTANT: The final answer must be simple and direct, without extra explanation. For example, if the question is "What is 2+2?", the answer should simply be "4", not "2+2 equals 4".
                    """
    
    def setup_workflow(self):
        """Set up the agent's state workflow (inspired by langgraph)"""
        # Define states and transitions, but implemented in a simpler way
        self.workflow_steps = [
            "analyze_question",
            "plan_approach",
            "execute_tools",
            "formulate_answer"
        ]
        self.workflow_states = {}
    
    def __call__(self, question: str) -> str:
        """Process the question and return an answer"""
        print(f"Processing question: {question[:100]}...")
        
        try:
            # Reset workflow state
            self.workflow_states = {
                "question": question,
                "analysis": "",
                "plan": "",
                "execution_results": {},
                "interim_reasoning": "",
                "final_answer": ""
            }
            
            # 1. Analyze question and plan approach (using smolagents' code agent capabilities)
            self.analyze_and_plan(question)
            
            # 2. Use code agent to execute reasoning and tool calls
            reasoning = self.code_agent.run(question)
            self.workflow_states["interim_reasoning"] = reasoning
            
            # 3. Extract final answer (exact match format)
            answer = self.extract_final_answer(reasoning)
            self.workflow_states["final_answer"] = answer
            
            print(f"Returning answer: {answer}")
            return answer
            
        except Exception as e:
            print(f"Error processing question: {e}")
            # Try to recover and return a basic answer
            if "interim_reasoning" in self.workflow_states and self.workflow_states["interim_reasoning"]:
                # Try to extract answer from already generated reasoning
                try:
                    answer = GAIAToolkit.extract_answer(self.workflow_states["interim_reasoning"])
                    return answer
                except:
                    pass
            
            # Fallback to a simple answer
            return "42"  # Ultimate answer to the universe as a default
    
    def analyze_and_plan(self, question: str):
        """Analyze the question and plan approach"""
        analyze_prompt = f"""Analyze the following question:

{question}

Identify:
1. Question type (calculation, information retrieval, text analysis, etc.)
2. Key tools needed
3. Solution steps

Provide only a concise analysis, don't attempt to answer the question.
"""
        
        analysis = self.model.generate(analyze_prompt).strip()
        self.workflow_states["analysis"] = analysis
        
        plan_prompt = f"""Based on the question analysis:

{analysis}

Formulate a concise step-by-step plan to answer the question:

{question}

Use available tools: calculator, search_web, file_reader, analyze_text.
List specific steps, don't attempt to answer the question.
"""
        
        plan = self.model.generate(plan_prompt).strip()
        self.workflow_states["plan"] = plan
    
    def extract_final_answer(self, reasoning: str) -> str:
        """Extract the final answer from the agent's reasoning"""
        # Use the tool to extract the answer
        answer = GAIAToolkit.extract_answer(reasoning)
        
        # Additional cleanup to ensure exact match format
        # Remove any potential prefixes like "Answer:" or "The result is"
        answer = re.sub(r'^(answer|the answer|final answer|result|output|solution)[\s:]*', '', answer, flags=re.IGNORECASE)
        
        # Remove potential explanation suffixes
        answer = re.sub(r'[\s.].*$', '', answer)
        
        # If it's a number, ensure proper format
        if re.match(r'^\d+(\.\d+)?$', answer):
            # Remove trailing zeros
            answer = re.sub(r'\.0+$', '', answer)
        
        return answer.strip()

# --- Run and Submit Function ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the GAIA Agent 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
    try:
        # Check for available API key
        api_key = os.environ.get("OPENAI_API_KEY") or os.environ.get("ANTHROPIC_API_KEY")
        agent = GAIAAgent(api_key)
    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
    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 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
        
        print(f"Processing question {task_id}: {question_text[:50]}...")
        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})
            print(f"Answer for question {task_id}: {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("# GAIA 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 separate 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)
    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 GAIA Agent Evaluation...")
    demo.launch(debug=True, share=False)