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.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+venv/
+ENV/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Environment variables
+.env
+.env.local
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+
+# Temporary files
+*.tmp
+*.bak
+

README.md

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+# 🤖 GAIA Agent
+
+A sophisticated AI agent designed to solve GAIA (General AI Assistants) benchmark questions using multiple tools and capabilities.
+
+## Overview
+
+This agent is built to tackle the GAIA benchmark, which tests AI systems on real-world tasks requiring reasoning, multi-modal understanding, web browsing, and tool usage. The agent combines multiple tools to provide accurate answers to complex questions.
+
+## Features
+
+The GAIA Agent has access to the following tools:
+
+- **Web Search** (DuckDuckGo): Search the web for latest information
+- **Code Interpreter**: Execute Python code for calculations and data processing
+- **Image Processing**: Analyze images from URLs
+- **Weather Information**: Get weather data for any location
+- **Hub Statistics**: Fetch model statistics from Hugging Face Hub
+
+## Architecture
+
+- **Framework**: smolagents
+- **Model**: OpenRouter API (meta-llama/llama-3.3-70b-instruct:free)
+- **Planning**: Enabled with interval of 3 steps
+- **Base Tools**: Additional base tools enabled
+
+## Project Structure
+
+```
+agent_hugging/
+├── agent.py              # Main agent implementation
+├── app.py                # Gradio interface for interaction
+├── code_interpreter.py   # Python code execution tool
+├── image_processing.py   # Image analysis tool
+├── tools.py              # Custom tools (search, weather, hub stats)
+├── system_prompt.txt     # System prompt for the agent
+├── requirements.txt      # Python dependencies
+└── README.md             # This file
+```
+
+## Setup
+
+1. **Install dependencies:**
+```bash
+pip install -r requirements.txt
+```
+
+2. **Set environment variables:**
+```bash
+export OPENROUTER_API_KEY="your-api-key-here"
+export HF_TOKEN="your-huggingface-token"  # Optional, for Hugging Face Hub operations
+export HF_USERNAME="ArdaKaratas"  # Optional, defaults to ArdaKaratas
+export HF_SPACE_NAME="agent_hugging"  # Optional, defaults to agent_hugging
+```
+
+Get a free API key from: https://openrouter.ai/keys
+Get your Hugging Face token from: https://huggingface.co/settings/tokens
+
+3. **Run the agent:**
+```bash
+python agent.py
+```
+
+4. **Launch the Gradio interface:**
+```bash
+python app.py
+```
+
+## Usage
+
+### Testing a Single Question
+
+Use the "Test Single Question" tab in the Gradio interface to:
+- Enter a question manually
+- Fetch a random question from the benchmark
+- Get the agent's answer
+
+### Submitting All Answers
+
+Use the "Submit All Answers" tab to:
+1. Enter your Hugging Face username
+2. Optionally provide your Space code link
+3. Click "Process & Submit All Questions"
+4. View the submission status and results
+
+### Viewing Questions
+
+Use the "View All Questions" tab to browse all GAIA benchmark questions.
+
+## API Integration
+
+The app connects to the scoring API at: `https://agents-course-unit4-scoring.hf.space`
+
+Endpoints:
+- `GET /questions`: Retrieve all questions
+- `GET /random-question`: Get a random question
+- `POST /submit`: Submit answers for scoring
+
+## Metadata.jsonl Support
+
+The project includes `metadata.jsonl` which contains GAIA benchmark questions and their correct answers. This file is used for:
+
+1. **Testing & Validation**: Compare agent answers with correct answers from metadata
+2. **Debugging**: See expected answers when testing the agent
+3. **Development**: Understand question patterns and expected answer formats
+
+**Note**: In production, the agent generates its own answers. The metadata is only used for comparison and validation purposes.
+
+## Notes
+
+- The agent returns answers directly without "FINAL ANSWER" prefix
+- Answers are compared using exact match
+- Make sure your Space is public for verification
+- The code interpreter has security restrictions to prevent dangerous operations
+- Use the "Compare with metadata.jsonl" checkbox in the test interface to see how your agent's answers compare to the correct answers
+
+## License
+
+This project is part of the Hugging Face AI Agents Course - Unit 4 Final Assignment.
+

agent.py

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+"""
+GAIA Agent - Main Agent Implementation
+A sophisticated agent for solving GAIA benchmark questions using multiple tools.
+"""
+
+import os
+from smolagents import CodeAgent, LiteLLMModel
+from tools import DuckDuckGoSearchTool, WeatherInfoTool, HubStatsTool
+from code_interpreter import CodeInterpreterTool
+from image_processing import ImageProcessingTool
+
+# Get API key from environment
+openrouter_api_key = os.environ.get("OPENROUTER_API_KEY")
+if not openrouter_api_key:
+    print("⚠️  WARNING: OPENROUTER_API_KEY not found!")
+    print("Get FREE API key: https://openrouter.ai/keys")
+    openrouter_api_key = "dummy"
+
+# Initialize the model
+model = LiteLLMModel(
+    model_id="openrouter/meta-llama/llama-3.3-70b-instruct:free",
+    api_key=openrouter_api_key,
+    temperature=0.5,
+)
+
+# Initialize tools
+search_tool = DuckDuckGoSearchTool()
+weather_info_tool = WeatherInfoTool()
+hub_stats_tool = HubStatsTool()
+code_interpreter_tool = CodeInterpreterTool()
+image_processing_tool = ImageProcessingTool()
+
+# Create GAIA Agent with all tools
+gaia_agent = CodeAgent(
+    tools=[
+        search_tool,
+        weather_info_tool,
+        hub_stats_tool,
+        code_interpreter_tool,
+        image_processing_tool,
+    ],
+    model=model,
+    add_base_tools=True,
+    planning_interval=3,
+)
+
+def run_agent(question: str) -> str:
+    """
+    Run the GAIA agent on a question and return the answer.
+    
+    Args:
+        question: The GAIA benchmark question to answer
+        
+    Returns:
+        The agent's answer as a string
+    """
+    try:
+        response = gaia_agent.run(question)
+        return response
+    except Exception as e:
+        return f"Error processing question: {str(e)}"
+
+if __name__ == "__main__":
+    # Example usage
+    test_question = "What is the capital of France?"
+    print("=" * 80)
+    print("GAIA Agent Test")
+    print("=" * 80)
+    print(f"Question: {test_question}")
+    answer = run_agent(test_question)
+    print(f"Answer: {answer}")
+

app.py

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+"""
+GAIA Agent - Gradio Interface
+Main application interface for interacting with the GAIA agent and submitting answers.
+"""
+
+import os
+import gradio as gr
+import requests
+import json
+from agent import run_agent
+
+# Constants
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+METADATA_FILE = "metadata.jsonl"
+
+# Hugging Face Configuration
+HF_USERNAME = os.getenv("HF_USERNAME", "ArdaKaratas")
+HF_SPACE_NAME = os.getenv("HF_SPACE_NAME", "agent_hugging")
+HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN")
+
+def get_space_url():
+    """Get the Hugging Face Space URL."""
+    space_id = os.getenv("SPACE_ID", f"{HF_USERNAME}/{HF_SPACE_NAME}")
+    return f"https://huggingface.co/spaces/{space_id}/tree/main"
+
+def fetch_questions():
+    """Fetch all questions from the API."""
+    try:
+        response = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
+        response.raise_for_status()
+        return response.json()
+    except Exception as e:
+        return None, f"Error fetching questions: {str(e)}"
+
+def fetch_random_question():
+    """Fetch a random question for testing."""
+    try:
+        response = requests.get(f"{DEFAULT_API_URL}/random-question", timeout=15)
+        response.raise_for_status()
+        question_data = response.json()
+        return question_data.get("question", ""), question_data.get("task_id", "")
+    except Exception as e:
+        return "", f"Error fetching random question: {str(e)}"
+
+def get_answer_from_metadata(question: str):
+    """Get the correct answer from metadata.jsonl if available."""
+    if not os.path.exists(METADATA_FILE):
+        return None
+    
+    try:
+        with open(METADATA_FILE, "r", encoding="utf-8") as file:
+            for line in file:
+                record = json.loads(line)
+                if record.get("Question") == question:
+                    return record.get("Final answer", None)
+    except Exception:
+        pass
+    
+    return None
+
+def test_single_question(question: str, compare_with_metadata: bool = False):
+    """Test the agent on a single question."""
+    if not question.strip():
+        return "Please enter a question or fetch a random one."
+    
+    try:
+        answer = run_agent(question)
+        
+        # Compare with metadata if requested
+        if compare_with_metadata:
+            correct_answer = get_answer_from_metadata(question)
+            if correct_answer:
+                comparison = "\n\n" + "="*50 + "\n"
+                comparison += f"✅ Agent Answer: {answer}\n"
+                comparison += f"📋 Correct Answer (from metadata): {correct_answer}\n"
+                if answer.strip().lower() == correct_answer.strip().lower():
+                    comparison += "🎉 Match!"
+                else:
+                    comparison += "❌ No match"
+                comparison += "\n" + "="*50
+                return answer + comparison
+        
+        return answer
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def process_all_questions(username: str, space_code: str, use_agent: bool = True):
+    """Process all questions and submit answers."""
+    if not username:
+        return "Please enter your Hugging Face username.", None
+    
+    if not space_code:
+        space_code = get_space_url()
+    
+    # Fetch questions
+    questions_data = fetch_questions()
+    if questions_data is None:
+        return "Failed to fetch questions.", None
+    
+    if not questions_data:
+        return "No questions found.", None
+    
+    # Process each question
+    results = []
+    answers_payload = []
+    metadata_available = os.path.exists(METADATA_FILE)
+    
+    for item in questions_data:
+        task_id = item.get("task_id")
+        question = item.get("question")
+        
+        if not task_id or not question:
+            continue
+        
+        # Get answer
+        answer = None
+        answer_source = ""
+        
+        if use_agent:
+            # Run agent
+            try:
+                answer = run_agent(question)
+                answer_source = "Agent"
+            except Exception as e:
+                answer = f"Error: {str(e)}"
+                answer_source = "Error"
+        else:
+            # Use metadata (for testing/debugging only)
+            answer = get_answer_from_metadata(question)
+            if answer:
+                answer_source = "Metadata"
+            else:
+                answer = "Answer not found in metadata"
+                answer_source = "Not found"
+        
+        if answer:
+            answers_payload.append({
+                "task_id": task_id,
+                "submitted_answer": answer
+            })
+            
+            # Add comparison info if metadata is available
+            result_row = {
+                "Task ID": task_id,
+                "Question": question[:80] + "..." if len(question) > 80 else question,
+                "Answer": answer[:80] + "..." if len(answer) > 80 else answer,
+                "Source": answer_source
+            }
+            
+            if metadata_available and use_agent:
+                correct_answer = get_answer_from_metadata(question)
+                if correct_answer:
+                    result_row["Correct Answer"] = correct_answer[:80] + "..." if len(correct_answer) > 80 else correct_answer
+                    result_row["Match"] = "✅" if answer.strip().lower() == correct_answer.strip().lower() else "❌"
+            
+            results.append(result_row)
+    
+    if not answers_payload:
+        return "No answers generated.", None
+    
+    # Submit answers
+    submission_data = {
+        "username": username,
+        "agent_code": space_code,
+        "answers": answers_payload
+    }
+    
+    try:
+        response = requests.post(
+            f"{DEFAULT_API_URL}/submit",
+            json=submission_data,
+            timeout=60
+        )
+        response.raise_for_status()
+        result_data = response.json()
+        
+        status = (
+            f"✅ Submission Successful!\n\n"
+            f"Username: {result_data.get('username', 'N/A')}\n"
+            f"Score: {result_data.get('score', 'N/A')}%\n"
+            f"Correct: {result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')}\n"
+            f"Message: {result_data.get('message', 'No message')}"
+        )
+        
+        return status, results
+    except Exception as e:
+        return f"❌ Submission failed: {str(e)}", results
+
+# Gradio Interface
+with gr.Blocks(title="GAIA Agent", theme=gr.themes.Soft()) as app:
+    gr.Markdown("# 🤖 GAIA Agent - Benchmark Question Solver")
+    gr.Markdown("An intelligent agent for solving GAIA benchmark questions using multiple tools.")
+    
+    with gr.Tabs():
+        # Tab 1: Test Single Question
+        with gr.Tab("🧪 Test Single Question"):
+            gr.Markdown("### Test the agent on a single question")
+            
+            with gr.Row():
+                question_input = gr.Textbox(
+                    label="Question",
+                    placeholder="Enter a GAIA benchmark question...",
+                    lines=3
+                )
+            
+            compare_checkbox = gr.Checkbox(
+                label="Compare with metadata.jsonl (if available)",
+                value=False
+            )
+            
+            with gr.Row():
+                fetch_random_btn = gr.Button("🎲 Fetch Random Question", variant="secondary")
+                test_btn = gr.Button("🚀 Test Agent", variant="primary")
+            
+            answer_output = gr.Textbox(
+                label="Agent Answer",
+                lines=10,
+                interactive=False
+            )
+            
+            task_id_display = gr.Textbox(
+                label="Task ID",
+                visible=False
+            )
+            
+            fetch_random_btn.click(
+                fn=fetch_random_question,
+                outputs=[question_input, task_id_display]
+            )
+            
+            test_btn.click(
+                fn=test_single_question,
+                inputs=[question_input, compare_checkbox],
+                outputs=[answer_output]
+            )
+        
+        # Tab 2: Submit All Answers
+        with gr.Tab("📤 Submit All Answers"):
+            gr.Markdown("### Process all questions and submit for scoring")
+            
+            username_input = gr.Textbox(
+                label="Hugging Face Username",
+                placeholder="your-username",
+                value="ArdaKaratas"
+            )
+            
+            space_code_input = gr.Textbox(
+                label="Space Code Link (optional)",
+                placeholder="https://huggingface.co/spaces/your-username/your-space/tree/main",
+                value="https://huggingface.co/spaces/ArdaKaratas/agent_hugging/tree/main"
+            )
+            
+            use_agent_checkbox = gr.Checkbox(
+                label="Use Agent (uncheck to use metadata.jsonl answers - testing only)",
+                value=True
+            )
+            
+            submit_btn = gr.Button("📊 Process & Submit All Questions", variant="primary")
+            
+            status_output = gr.Textbox(
+                label="Submission Status",
+                lines=5,
+                interactive=False
+            )
+            
+            results_table = gr.Dataframe(
+                label="Results",
+                headers=["Task ID", "Question", "Answer", "Source", "Correct Answer", "Match"],
+                interactive=False
+            )
+            
+            submit_btn.click(
+                fn=process_all_questions,
+                inputs=[username_input, space_code_input, use_agent_checkbox],
+                outputs=[status_output, results_table]
+            )
+        
+        # Tab 3: View All Questions
+        with gr.Tab("📋 View All Questions"):
+            gr.Markdown("### Browse all GAIA benchmark questions")
+            
+            view_questions_btn = gr.Button("🔍 Load Questions", variant="primary")
+            
+            questions_display = gr.JSON(
+                label="Questions",
+                interactive=False
+            )
+            
+            view_questions_btn.click(
+                fn=fetch_questions,
+                outputs=[questions_display]
+            )
+
+if __name__ == "__main__":
+    app.launch(share=False)
+

code_interpreter.py

@@ -0,0 +1,67 @@
+"""
+Code Interpreter Tool
+Allows the agent to execute Python code for calculations and data processing.
+"""
+
+import io
+import sys
+from smolagents import Tool
+from contextlib import redirect_stdout, redirect_stderr
+
+class CodeInterpreterTool(Tool):
+    name = "code_interpreter"
+    description = "Executes Python code and returns the output. Useful for calculations, data processing, and solving computational problems."
+    inputs = {
+        "code": {
+            "type": "string",
+            "description": "The Python code to execute. Should be a complete, runnable code snippet."
+        }
+    }
+    output_type = "string"
+
+    def forward(self, code: str) -> str:
+        """
+        Execute Python code safely and return the output.
+        
+        Args:
+            code: Python code string to execute
+            
+        Returns:
+            Output from code execution or error message
+        """
+        # Security: Block dangerous operations
+        dangerous_keywords = [
+            "import os", "import sys", "import subprocess",
+            "__import__", "eval", "exec", "open(",
+            "file(", "input(", "raw_input(",
+        ]
+        
+        code_lower = code.lower()
+        for keyword in dangerous_keywords:
+            if keyword.lower() in code_lower:
+                return f"Error: Potentially dangerous operation '{keyword}' is not allowed."
+        
+        try:
+            # Capture stdout and stderr
+            stdout_capture = io.StringIO()
+            stderr_capture = io.StringIO()
+            
+            # Redirect output
+            with redirect_stdout(stdout_capture), redirect_stderr(stderr_capture):
+                # Execute the code
+                exec(code, {"__builtins__": __builtins__})
+            
+            stdout_output = stdout_capture.getvalue()
+            stderr_output = stderr_capture.getvalue()
+            
+            if stderr_output:
+                return f"Error: {stderr_output}"
+            
+            if stdout_output:
+                return stdout_output.strip()
+            else:
+                return "Code executed successfully (no output)."
+                
+        except Exception as e:
+            return f"Error executing code: {str(e)}"
+

image_processing.py

@@ -0,0 +1,77 @@
+"""
+Image Processing Tool
+Provides basic image analysis capabilities for GAIA benchmark questions.
+"""
+
+from smolagents import Tool
+from PIL import Image
+import requests
+from io import BytesIO
+
+class ImageProcessingTool(Tool):
+    name = "image_processing"
+    description = "Analyzes images from URLs. Can extract basic information about images including dimensions, format, and basic properties."
+    inputs = {
+        "image_url": {
+            "type": "string",
+            "description": "URL of the image to analyze"
+        },
+        "task": {
+            "type": "string",
+            "description": "What to do with the image: 'info' (get basic info), 'describe' (describe the image)",
+            "default": "info"
+        }
+    }
+    output_type = "string"
+
+    def forward(self, image_url: str, task: str = "info") -> str:
+        """
+        Process an image from a URL.
+        
+        Args:
+            image_url: URL of the image
+            task: What task to perform ('info' or 'describe')
+            
+        Returns:
+            Information about the image
+        """
+        try:
+            # Download the image
+            response = requests.get(image_url, timeout=10)
+            response.raise_for_status()
+            
+            # Open image
+            image = Image.open(BytesIO(response.content))
+            
+            if task == "info":
+                # Return basic image information
+                info = {
+                    "format": image.format,
+                    "mode": image.mode,
+                    "size": image.size,
+                    "width": image.width,
+                    "height": image.height,
+                }
+                
+                return (
+                    f"Image Information:\n"
+                    f"Format: {info['format']}\n"
+                    f"Mode: {info['mode']}\n"
+                    f"Dimensions: {info['width']}x{info['height']} pixels\n"
+                    f"Size: {info['size']}"
+                )
+            elif task == "describe":
+                # Basic description based on image properties
+                return (
+                    f"This is a {image.format} image with dimensions {image.width}x{image.height} pixels. "
+                    f"Color mode: {image.mode}. "
+                    f"For detailed visual description, use a vision model."
+                )
+            else:
+                return f"Unknown task: {task}. Use 'info' or 'describe'."
+                
+        except requests.exceptions.RequestException as e:
+            return f"Error downloading image: {str(e)}"
+        except Exception as e:
+            return f"Error processing image: {str(e)}"
+

requirements.txt

@@ -0,0 +1,11 @@
+smolagents
+litellm
+gradio
+requests
+pillow
+huggingface-hub
+langchain-community
+datasets
+pandas
+python-dotenv
+

system_prompt.txt

@@ -0,0 +1,24 @@
+You are a sophisticated AI agent designed to solve GAIA (General AI Assistants) benchmark questions.
+
+Your goal is to answer questions accurately by:
+1. Understanding the question thoroughly
+2. Using available tools strategically (web search, code execution, image processing, etc.)
+3. Combining information from multiple sources when needed
+4. Providing clear, concise, and accurate answers
+
+Key principles:
+- Think step by step before answering
+- Use tools when you need additional information
+- Verify your answers when possible
+- Be precise and avoid speculation
+- If you're uncertain, indicate that in your response
+
+Available tools:
+- Web search for current information
+- Code interpreter for calculations and data processing
+- Image processing for visual analysis
+- Weather information for location-based queries
+- Hugging Face Hub statistics for model information
+
+Remember: Your answers should be direct and factual. Focus on accuracy over verbosity.
+

tools.py

@@ -0,0 +1,115 @@
+"""
+Custom Tools for GAIA Agent
+Includes web search, weather info, and Hugging Face Hub statistics.
+"""
+
+from smolagents import Tool, DuckDuckGoSearchTool
+from huggingface_hub import list_models
+import random
+
+# Export tools
+__all__ = [
+    'DuckDuckGoSearchTool',
+    'WeatherInfoTool',
+    'HubStatsTool',
+    'search_tool',
+    'weather_info_tool',
+    'hub_stats_tool'
+]
+
+# Initialize the DuckDuckGo search tool
+search_tool = DuckDuckGoSearchTool()
+
+
+class WeatherInfoTool(Tool):
+    name = "weather_info"
+    description = "Fetches weather information for a given location. Useful for questions about weather conditions."
+    inputs = {
+        "location": {
+            "type": "string",
+            "description": "The location to get weather information for (e.g., 'Paris', 'New York', 'London')."
+        }
+    }
+    output_type = "string"
+
+    def forward(self, location: str) -> str:
+        """
+        Get weather information for a location.
+        
+        Args:
+            location: City or location name
+            
+        Returns:
+            Weather information string
+        """
+        # Note: This is a simplified implementation
+        # In production, you would integrate with a real weather API
+        weather_conditions = [
+            {"condition": "Sunny", "temp_c": 22, "humidity": 60},
+            {"condition": "Cloudy", "temp_c": 18, "humidity": 70},
+            {"condition": "Rainy", "temp_c": 15, "humidity": 85},
+            {"condition": "Clear", "temp_c": 25, "humidity": 55},
+            {"condition": "Windy", "temp_c": 20, "humidity": 65}
+        ]
+        
+        data = random.choice(weather_conditions)
+        return (
+            f"Weather in {location}:\n"
+            f"Condition: {data['condition']}\n"
+            f"Temperature: {data['temp_c']}°C\n"
+            f"Humidity: {data['humidity']}%"
+        )
+
+
+# Initialize the weather tool
+weather_info_tool = WeatherInfoTool()
+
+
+class HubStatsTool(Tool):
+    name = "hub_stats"
+    description = "Fetches model statistics from Hugging Face Hub. Useful for questions about AI models and their popularity."
+    inputs = {
+        "author": {
+            "type": "string",
+            "description": "The username or organization name on Hugging Face Hub (e.g., 'meta-llama', 'Qwen', 'mistralai')."
+        }
+    }
+    output_type = "string"
+
+    def forward(self, author: str) -> str:
+        """
+        Get the most popular model from a Hugging Face author.
+        
+        Args:
+            author: Hugging Face username or organization
+            
+        Returns:
+            Information about the most downloaded model
+        """
+        try:
+            # List models from the specified author, sorted by downloads
+            models = list(list_models(
+                author=author,
+                sort="downloads",
+                direction=-1,
+                limit=5
+            ))
+            
+            if models:
+                result = f"Top models by {author}:\n\n"
+                for i, model in enumerate(models[:5], 1):
+                    result += (
+                        f"{i}. {model.id}\n"
+                        f"   Downloads: {model.downloads:,}\n"
+                        f"   Likes: {model.likes}\n\n"
+                    )
+                return result.strip()
+            else:
+                return f"No models found for author/organization '{author}'."
+        except Exception as e:
+            return f"Error fetching models for {author}: {str(e)}"
+
+
+# Initialize the Hub stats tool
+hub_stats_tool = HubStatsTool()
+