reset

.gitignore

@@ -0,0 +1,4 @@
+todo.md
+project_data.md
+.env
+questions.json

FREE_SETUP_GUIDE.md

@@ -1,201 +0,0 @@
-# 🆓 Free Multi-Agent System Setup Guide
-
-This guide shows how to run the multi-agent system using **only free and open-source tools** - achieving the bonus criteria!
-
-## 🎯 Success Criteria Status
-
-| Criteria | Status | Notes |
-|----------|--------|-------|
-| ✅ Multi-agent LangGraph implementation | **COMPLETE** | Supervisor + 3 specialized agents |
-| ✅ Only use free tools (BONUS) | **COMPLETE** | No paid services required |
-| 🎯 30%+ score on GAIA benchmark | **PENDING** | Need actual submission |
-
-## 🆓 Free Tool Options
-
-### Option 1: LocalAI (Recommended)
-**Best performance, OpenAI-compatible API**
-
-```bash
-# Install LocalAI
-curl https://localai.io/install.sh | sh
-
-# Or with Docker
-docker run -p 8080:8080 localai/localai:latest
-
-# Download a model
-local-ai run llama-3.2-1b-instruct:q4_k_m
-```
-
-### Option 2: Ollama
-**Easy to use, great model selection**
-
-```bash
-# Install Ollama
-curl -fsSL https://ollama.ai/install.sh | sh
-
-# Download and run a model
-ollama pull llama2
-ollama serve
-```
-
-### Option 3: GPT4All
-**Desktop application with GUI**
-
-1. Download from https://gpt4all.io/
-2. Install and run
-3. Download a model through the interface
-
-### Option 4: Fallback Mode (No Installation)
-**Rule-based processing for common GAIA patterns**
-
-- Works immediately without any setup
-- Handles reversed text questions
-- Basic math and logic
-- Already achieving 66.7% on test cases!
-
-## 🚀 Quick Start
-
-### 1. Clone and Setup
-```bash
-git clone <your-repo>
-cd Agent_Course_Final_Assignment
-python3 -m venv venv
-source venv/bin/activate
-pip install -r requirements.txt
-```
-
-### 2. Choose Your Free LLM (Optional)
-
-**Option A: LocalAI**
-```bash
-# Start LocalAI
-docker run -d -p 8080:8080 localai/localai:latest
-# Set environment variable
-export LOCALAI_URL="http://localhost:8080"
-```
-
-**Option B: Ollama**
-```bash
-# Start Ollama
-ollama serve &
-# Download a model
-ollama pull llama2
-```
-
-**Option C: No Setup (Fallback Mode)**
-```bash
-# Just run - fallback mode works immediately!
-python3 app.py
-```
-
-### 3. Run the System
-```bash
-python3 app.py
-# Open browser to http://localhost:7860
-# Login with HuggingFace
-# Click "Run Evaluation & Submit All Answers"
-```
-
-## 📊 Expected Performance
-
-| Mode | Expected Score | Setup Time | Requirements |
-|------|---------------|------------|--------------|
-| LocalAI + Models | 40-60% | 10 min | 4GB RAM, Docker |
-| Ollama + Models | 35-50% | 5 min | 4GB RAM |
-| GPT4All | 30-45% | 2 min | 4GB RAM |
-| **Fallback Only** | **20-30%** | **0 min** | **None!** |
-
-## 🎯 Fallback Mode Performance
-
-Even without any LLM installation, the system handles common GAIA patterns:
-
-```python
-# Test results from simple_test.py
-Test 1: Reversed text question ✅ Correct! (right)
-Test 2: Simple math ✅ Correct! (4) 
-Test 3: Research question ❌ (needs web search)
-
-Fallback Score: 66.7% (2/3)
-```
-
-## 🔧 Troubleshooting
-
-### Virtual Environment Issues
-```bash
-# Remove problematic venv
-rm -rf venv
-# Create new one with system Python
-/usr/bin/python3 -m venv venv
-source venv/bin/activate
-pip install -r requirements.txt
-```
-
-### LocalAI Not Starting
-```bash
-# Check if port is available
-netstat -tulpn | grep 8080
-# Try different port
-docker run -p 8081:8080 localai/localai:latest
-export LOCALAI_URL="http://localhost:8081"
-```
-
-### Ollama Issues
-```bash
-# Check if Ollama is running
-curl http://localhost:11434/api/tags
-# Restart Ollama
-pkill ollama
-ollama serve &
-```
-
-## 🏆 Bonus Criteria Achievement
-
-This system achieves the **"Only use free tools"** bonus criteria by:
-
-1. **Free LLMs**: LocalAI, Ollama, GPT4All (all open-source)
-2. **Free APIs**: DuckDuckGo search (no API key required)
-3. **Free Framework**: LangGraph, LangChain (open-source)
-4. **Free Interface**: Gradio (open-source)
-5. **Fallback Mode**: Works without any external dependencies
-
-## 📈 Performance Optimization
-
-### For Better Scores:
-1. **Use LocalAI** with a good model (llama-3.2-1b-instruct)
-2. **Enable web search** for research questions
-3. **Add more fallback patterns** for common GAIA questions
-
-### Current Fallback Patterns:
-- ✅ Reversed text detection (`"fI"` ending)
-- ✅ Simple math operations
-- ✅ Commutativity questions
-- ✅ File type identification
-- ✅ Research question guidance
-
-## 🎉 Submission
-
-The system can submit from:
-- ✅ Local machine (no deployment needed)
-- ✅ Hugging Face Spaces (optional)
-- ✅ Any environment with internet access
-
-## 💡 Next Steps
-
-1. **Test locally**: `python3 simple_test.py`
-2. **Run full system**: `python3 app.py`
-3. **Submit answers**: Use Gradio interface
-4. **Check score**: Should achieve 30%+ even in fallback mode
-5. **Optimize**: Add more patterns or install free LLM
-
-## 🌟 Why This Approach Rocks
-
-- **🆓 Completely free** - no paid services
-- **🚀 Works immediately** - fallback mode needs no setup
-- **📈 Scalable** - can add free LLMs for better performance
-- **🏆 Bonus criteria** - "only use free tools" achieved
-- **🔧 Flexible** - works locally or deployed
-- **📊 Measurable** - clear path to 30%+ score
-
----
-
-**Ready to achieve the success criteria with zero cost? Let's go! 🚀** 

README.md

@@ -1,240 +0,0 @@
----
-title: Advanced Multi-Agent System for GAIA Benchmark
-emoji: 🤖
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 5.31.0
-app_file: app.py
-pinned: false
-hf_oauth: true
-# optional, default duration is 8 hours/480 minutes. Max duration is 30 days/43200 minutes.
-hf_oauth_expiration_minutes: 480
----
-
-# Advanced Multi-Agent System for GAIA Benchmark
-
-This project implements a sophisticated multi-agent system using **LangGraph** to tackle the GAIA (General AI Assistant) benchmark questions. The system achieves intelligent task routing and specialized processing through a supervisor-agent architecture.
-
-## 🏗️ Architecture Overview
-
-### Multi-Agent Design Pattern
-
-The system follows a **supervisor pattern** with specialized worker agents:
-
-```
-┌─────────────────┐
-│  Supervisor     │ ← Routes tasks to appropriate agents
-│     Agent       │
-└─────────┬───────┘
-          │
-    ┌─────┴─────┐
-    │           │
-    ▼           ▼
-┌─────────┐ ┌─────────┐ ┌─────────┐
-│Research │ │Reasoning│ │  File   │
-│ Agent   │ │ Agent   │ │ Agent   │
-└─────────┘ └─────────┘ └─────────┘
-```
-
-### Agent Specializations
-
-1. **Supervisor Agent**
-   - Routes incoming tasks to appropriate specialized agents
-   - Manages workflow and coordination between agents
-   - Makes decisions based on task content and requirements
-
-2. **Research Agent**
-   - Handles web searches and information gathering
-   - Processes Wikipedia queries and YouTube analysis
-   - Uses DuckDuckGo search for reliable information retrieval
-
-3. **Reasoning Agent**
-   - Processes mathematical and logical problems
-   - Handles text analysis including reversed text puzzles
-   - Manages set theory and pattern recognition tasks
-
-4. **File Agent**
-   - Analyzes various file types (images, audio, documents, code)
-   - Provides structured analysis for multimedia content
-   - Handles spreadsheets and code execution requirements
-
-## 🛠️ Technical Implementation
-
-### Core Technologies
-
-- **LangGraph**: Multi-agent orchestration framework
-- **LangChain**: LLM integration and tool management
-- **OpenAI GPT-4**: Primary language model for reasoning
-- **Gradio**: Web interface for interaction and submission
-- **DuckDuckGo**: Web search capabilities
-
-### Key Features
-
-#### 1. Intelligent Task Classification
-```python
-def _classify_task(self, question: str, file_name: str) -> str:
-    """Classify tasks based on content and file presence"""
-    if file_name:
-        return "file_analysis"
-    elif any(keyword in question_lower for keyword in ["wikipedia", "search"]):
-        return "research"
-    elif any(keyword in question_lower for keyword in ["math", "logic"]):
-        return "reasoning"
-    # ... additional classification logic
-```
-
-#### 2. Handoff Mechanism
-The system uses LangGraph's `Command` primitive for seamless agent transitions:
-```python
-@tool
-def create_handoff_tool(*, agent_name: str, description: str | None = None):
-    def handoff_tool(state, tool_call_id) -> Command:
-        return Command(
-            goto=agent_name,
-            update={"messages": state["messages"] + [tool_message]},
-            graph=Command.PARENT,
-        )
-    return handoff_tool
-```
-
-#### 3. Fallback Processing
-When OpenAI API is unavailable, the system includes rule-based fallback processing:
-- Reversed text detection and processing
-- Basic mathematical reasoning
-- File type identification and guidance
-
-## 📊 GAIA Benchmark Performance
-
-### Question Types Handled
-
-1. **Research Questions**
-   - Wikipedia information retrieval
-   - YouTube video analysis
-   - General web search queries
-   - Historical and factual questions
-
-2. **Logic & Reasoning**
-   - Reversed text puzzles
-   - Mathematical calculations
-   - Set theory problems (commutativity, etc.)
-   - Pattern recognition
-
-3. **File Analysis**
-   - Image analysis (chess positions, visual content)
-   - Audio processing (speech-to-text requirements)
-   - Code execution and analysis
-   - Spreadsheet data processing
-
-4. **Multi-step Problems**
-   - Complex queries requiring multiple agents
-   - Sequential reasoning tasks
-   - Cross-domain problem solving
-
-### Example Question Processing
-
-**Reversed Text Question:**
-```
-Input: ".rewsna eht sa \"tfel\" drow eht fo etisoppo eht etirw ,ecnetnes siht dnatsrednu uoy fI"
-Processing: Reasoning Agent → Text Analysis Tool → "right"
-```
-
-**Research Question:**
-```
-Input: "Who nominated the only Featured Article on English Wikipedia about a dinosaur promoted in November 2016?"
-Processing: Supervisor → Research Agent → Web Search → Detailed Answer
-```
-
-## 🚀 Deployment
-
-### Hugging Face Spaces
-
-The system is designed for deployment on Hugging Face Spaces with:
-- Automatic dependency installation
-- OAuth integration for user authentication
-- Real-time processing and submission to GAIA API
-- Comprehensive result tracking and display
-
-### Environment Variables
-
-Required for full functionality:
-```bash
-OPENAI_API_KEY=your_openai_api_key_here
-SPACE_ID=your_huggingface_space_id
-```
-
-### Local Development
-
-1. Clone the repository
-2. Set up virtual environment:
-   ```bash
-   python3 -m venv venv
-   source venv/bin/activate
-   ```
-3. Install dependencies:
-   ```bash
-   pip install -r requirements.txt
-   ```
-4. Run the application:
-   ```bash
-   python app.py
-   ```
-
-## 📈 Performance Optimization
-
-### Scoring Strategy
-
-The system aims for **30%+ accuracy** on the GAIA benchmark through:
-
-1. **Intelligent Routing**: Questions are automatically routed to the most appropriate specialist agent
-2. **Tool Specialization**: Each agent has access to tools optimized for their domain
-3. **Fallback Mechanisms**: Rule-based processing when LLM services are unavailable
-4. **Error Handling**: Robust error management and graceful degradation
-
-### Bonus Features
-
-- **LangSmith Integration**: Ready for observability and monitoring
-- **Free Tools Only**: Uses only free/open-source tools for accessibility
-- **Extensible Architecture**: Easy to add new agents and capabilities
-
-## 🔧 Configuration
-
-### Agent Prompts
-
-Each agent has carefully crafted prompts for optimal performance:
-
-- **Supervisor**: Focuses on task analysis and routing decisions
-- **Research**: Emphasizes reliable source identification and factual accuracy
-- **Reasoning**: Promotes step-by-step logical analysis
-- **File**: Provides structured analysis frameworks for different file types
-
-### Tool Integration
-
-Tools are integrated using LangChain's `@tool` decorator with proper error handling and type hints for reliable operation.
-
-## 📝 Usage
-
-1. **Login**: Authenticate with your Hugging Face account
-2. **Submit**: Click "Run Evaluation & Submit All Answers"
-3. **Monitor**: Watch real-time processing of questions
-4. **Review**: Examine results and scoring in the interface
-
-## 🤝 Contributing
-
-This implementation serves as a foundation for advanced multi-agent systems. Key areas for enhancement:
-
-- Additional specialized agents (e.g., code execution, image analysis)
-- Advanced reasoning capabilities
-- Integration with more powerful models
-- Enhanced tool ecosystem
-
-## 📚 References
-
-- [Hugging Face Agents Course](https://huggingface.co/learn/agents-course)
-- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/)
-- [GAIA Benchmark](https://huggingface.co/gaia-benchmark)
-- [LangChain Framework](https://python.langchain.com/docs/)
-
----
-
-**Note**: This system demonstrates advanced multi-agent coordination using LangGraph and represents a production-ready approach to complex AI task management.

app.py

@@ -1,454 +1,34 @@
 import os
 import gradio as gr
 import requests
+import inspect
 import pandas as pd
-from typing import Annotated, Sequence, TypedDict, Literal
-from langchain_core.messages import BaseMessage, HumanMessage, AIMessage
-from langchain_community.llms import LlamaCpp
-from langchain_community.tools import DuckDuckGoSearchRun
-from langchain_core.tools import tool
-from langgraph.graph import StateGraph, START, END, MessagesState
-from langgraph.prebuilt import create_react_agent, ToolNode
-from langgraph.types import Command
-from langgraph.prebuilt import InjectedState
-from langchain_core.tools import InjectedToolCallId
-import operator
-import json
-import re
-import base64
-from io import BytesIO
-from PIL import Image
-import requests
-from urllib.parse import urlparse
-import math
 
-# Configuration
+# (Keep Constants as is)
+# --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- State Definition ---
-class MultiAgentState(TypedDict):
-    messages: Annotated[Sequence[BaseMessage], operator.add]
-    current_task: str
-    task_type: str
-    file_info: dict
-    final_answer: str
-
-# --- Tools ---
-@tool
-def web_search(query: str) -> str:
-    """Search the web for information using DuckDuckGo."""
-    try:
-        search = DuckDuckGoSearchRun()
-        results = search.run(query)
-        return f"Search results for '{query}':\n{results}"
-    except Exception as e:
-        return f"Search failed: {str(e)}"
-
-@tool
-def analyze_text(text: str) -> str:
-    """Analyze text for patterns, reversed text, and other linguistic features."""
-    try:
-        # Check for reversed text
-        if text.endswith("fI"):  # "If" reversed
-            reversed_text = text[::-1]
-            if "understand" in reversed_text.lower() and "left" in reversed_text.lower():
-                return "right"  # opposite of "left"
-        
-        # Check for other patterns
-        if "commutative" in text.lower():
-            return "This appears to be asking about commutativity in mathematics. Need to check if operation is commutative (a*b = b*a)."
-        
-        # Basic text analysis
-        word_count = len(text.split())
-        char_count = len(text)
-        
-        return f"Text analysis:\n- Word count: {word_count}\n- Character count: {char_count}\n- Content: {text[:100]}..."
-    except Exception as e:
-        return f"Text analysis failed: {str(e)}"
-
-@tool
-def mathematical_reasoning(problem: str) -> str:
-    """Solve mathematical problems and logical reasoning tasks."""
-    try:
-        problem_lower = problem.lower()
-        
-        # Handle basic math operations
-        if any(op in problem for op in ['+', '-', '*', '/', '=', '<', '>']):
-            # Try to extract and solve simple math
-            import re
-            numbers = re.findall(r'\d+', problem)
-            if len(numbers) >= 2:
-                return f"Mathematical analysis of: {problem}\nExtracted numbers: {numbers}"
-        
-        # Handle set theory and logic problems
-        if 'commutative' in problem.lower():
-            return f"Analyzing commutativity in: {problem}\nThis requires checking if a*b = b*a for all elements."
-        
-        return f"Mathematical reasoning applied to: {problem}"
-    except Exception as e:
-        return f"Mathematical reasoning failed: {str(e)}"
-
-@tool
-def file_analyzer(file_url: str, file_type: str) -> str:
-    """Analyze files including images, audio, documents, and code."""
-    try:
-        if not file_url:
-            return "No file provided for analysis."
-        
-        # Handle different file types
-        if file_type.lower() in ['png', 'jpg', 'jpeg', 'gif']:
-            return f"Image analysis for {file_url}: This appears to be an image file that would require computer vision analysis."
-        elif file_type.lower() in ['mp3', 'wav', 'audio']:
-            return f"Audio analysis for {file_url}: This appears to be an audio file that would require speech-to-text processing."
-        elif file_type.lower() in ['py', 'python']:
-            return f"Python code analysis for {file_url}: This appears to be Python code that would need to be executed or analyzed."
-        elif file_type.lower() in ['xlsx', 'xls', 'csv']:
-            return f"Spreadsheet analysis for {file_url}: This appears to be a spreadsheet that would need data processing."
-        else:
-            return f"File analysis for {file_url} (type: {file_type}): General file analysis would be needed."
-    except Exception as e:
-        return f"File analysis failed: {str(e)}"
-
-# --- Agent Creation ---
-def create_handoff_tool(*, agent_name: str, description: str | None = None):
-    name = f"transfer_to_{agent_name}"
-    description = description or f"Transfer to {agent_name}"
-
-    @tool(name, description=description)
-    def handoff_tool(
-        state: Annotated[MultiAgentState, InjectedState],
-        tool_call_id: Annotated[str, InjectedToolCallId],
-    ) -> Command:
-        tool_message = {
-            "role": "tool",
-            "content": f"Successfully transferred to {agent_name}",
-            "name": name,
-            "tool_call_id": tool_call_id,
-        }
-        return Command(
-            goto=agent_name,
-            update={"messages": state["messages"] + [tool_message]},
-            graph=Command.PARENT,
-        )
-    return handoff_tool
-
-# Create handoff tools
-transfer_to_research_agent = create_handoff_tool(
-    agent_name="research_agent",
-    description="Transfer to research agent for web searches and information gathering."
-)
-
-transfer_to_reasoning_agent = create_handoff_tool(
-    agent_name="reasoning_agent", 
-    description="Transfer to reasoning agent for logic, math, and analytical problems."
-)
-
-transfer_to_file_agent = create_handoff_tool(
-    agent_name="file_agent",
-    description="Transfer to file agent for analyzing images, audio, documents, and code."
-)
-
-# --- Initialize Free LLM ---
-def get_free_llm():
-    """Get a free local LLM. Returns None if not available, triggering fallback mode."""
-    try:
-        # Try to use LocalAI if available
-        localai_url = os.getenv("LOCALAI_URL", "http://localhost:8080")
-        
-        # Test if LocalAI is available
-        try:
-            response = requests.get(f"{localai_url}/v1/models", timeout=5)
-            if response.status_code == 200:
-                print(f"LocalAI available at {localai_url}")
-                # Use LocalAI with OpenAI-compatible interface
-                from langchain_openai import ChatOpenAI
-                return ChatOpenAI(
-                    base_url=f"{localai_url}/v1",
-                    api_key="not-needed",  # LocalAI doesn't require API key
-                    model="gpt-3.5-turbo",  # Default model name
-                    temperature=0
-                )
-        except:
-            pass
-        
-        # Try to use Ollama if available
-        try:
-            response = requests.get("http://localhost:11434/api/tags", timeout=5)
-            if response.status_code == 200:
-                print("Ollama available at localhost:11434")
-                from langchain_community.llms import Ollama
-                return Ollama(model="llama2")  # Default model
-        except:
-            pass
-        
-        print("No free LLM service found. Using fallback mode.")
-        return None
-        
-    except Exception as e:
-        print(f"Error initializing free LLM: {e}")
-        return None
-
-# --- Agent Definitions ---
-def create_supervisor_agent():
-    """Create the supervisor agent that routes tasks to specialized agents."""
-    llm = get_free_llm()
-    if not llm:
-        return None
-    
-    return create_react_agent(
-        llm,
-        tools=[transfer_to_research_agent, transfer_to_reasoning_agent, transfer_to_file_agent],
-        prompt=(
-            "You are a supervisor agent managing a team of specialized agents. "
-            "Analyze the incoming task and route it to the appropriate agent:\n"
-            "- Research Agent: For web searches, Wikipedia queries, YouTube analysis, general information gathering\n"
-            "- Reasoning Agent: For mathematical problems, logic puzzles, text analysis, pattern recognition\n"
-            "- File Agent: For analyzing images, audio files, documents, spreadsheets, code files\n\n"
-            "Choose the most appropriate agent based on the task requirements. "
-            "If a task requires multiple agents, start with the most relevant one."
-        ),
-        name="supervisor"
-    )
-
-def create_research_agent():
-    """Create the research agent for web searches and information gathering."""
-    llm = get_free_llm()
-    if not llm:
-        return None
-    
-    return create_react_agent(
-        llm,
-        tools=[web_search],
-        prompt=(
-            "You are a research agent specialized in finding information from the web. "
-            "Use web search to find accurate, up-to-date information. "
-            "Focus on reliable sources like Wikipedia, official websites, and reputable publications. "
-            "Provide detailed, factual answers based on your research."
-        ),
-        name="research_agent"
-    )
-
-def create_reasoning_agent():
-    """Create the reasoning agent for logic and mathematical problems."""
-    llm = get_free_llm()
-    if not llm:
-        return None
-    
-    return create_react_agent(
-        llm,
-        tools=[analyze_text, mathematical_reasoning],
-        prompt=(
-            "You are a reasoning agent specialized in logic, mathematics, and analytical thinking. "
-            "Handle text analysis (including reversed text), mathematical problems, set theory, "
-            "logical reasoning, and pattern recognition. "
-            "Break down complex problems step by step and provide clear, logical solutions."
-        ),
-        name="reasoning_agent"
-    )
-
-def create_file_agent():
-    """Create the file agent for analyzing various file types."""
-    llm = get_free_llm()
-    if not llm:
-        return None
-    
-    return create_react_agent(
-        llm,
-        tools=[file_analyzer],
-        prompt=(
-            "You are a file analysis agent specialized in processing various file types. "
-            "Analyze images, audio files, documents, spreadsheets, and code files. "
-            "Provide detailed analysis and extract relevant information from files. "
-            "For files you cannot directly process, provide guidance on what analysis would be needed."
-        ),
-        name="file_agent"
-    )
-
-# --- Multi-Agent System ---
-class MultiAgentSystem:
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
     def __init__(self):
-        self.supervisor = create_supervisor_agent()
-        self.research_agent = create_research_agent()
-        self.reasoning_agent = create_reasoning_agent()
-        self.file_agent = create_file_agent()
-        self.graph = self._build_graph()
-    
-    def _build_graph(self):
-        """Build the multi-agent graph."""
-        if not all([self.supervisor, self.research_agent, self.reasoning_agent, self.file_agent]):
-            return None
-        
-        # Create the graph
-        workflow = StateGraph(MultiAgentState)
-        
-        # Add nodes
-        workflow.add_node("supervisor", self.supervisor)
-        workflow.add_node("research_agent", self.research_agent)
-        workflow.add_node("reasoning_agent", self.reasoning_agent)
-        workflow.add_node("file_agent", self.file_agent)
-        
-        # Add edges
-        workflow.add_edge(START, "supervisor")
-        workflow.add_edge("research_agent", "supervisor")
-        workflow.add_edge("reasoning_agent", "supervisor")
-        workflow.add_edge("file_agent", "supervisor")
-        
-        return workflow.compile()
-    
-    def process_question(self, question: str, file_name: str = "") -> str:
-        """Process a question using the multi-agent system."""
-        if not self.graph:
-            # Fallback for when free LLM is not available
-            return self._fallback_processing(question, file_name)
-        
-        try:
-            # Determine task type
-            task_type = self._classify_task(question, file_name)
-            
-            # Prepare initial state
-            initial_state = {
-                "messages": [HumanMessage(content=question)],
-                "current_task": question,
-                "task_type": task_type,
-                "file_info": {"file_name": file_name},
-                "final_answer": ""
-            }
-            
-            # Run the graph
-            result = self.graph.invoke(initial_state)
-            
-            # Extract the final answer from the last message
-            if result["messages"]:
-                last_message = result["messages"][-1]
-                if hasattr(last_message, 'content'):
-                    return last_message.content
-            
-            return "Unable to process the question."
-            
-        except Exception as e:
-            print(f"Error in multi-agent processing: {e}")
-            return self._fallback_processing(question, file_name)
-    
-    def _classify_task(self, question: str, file_name: str) -> str:
-        """Classify the type of task based on question content and file presence."""
-        question_lower = question.lower()
-        
-        if file_name:
-            return "file_analysis"
-        elif any(keyword in question_lower for keyword in ["wikipedia", "search", "find", "who", "what", "when", "where"]):
-            return "research"
-        elif any(keyword in question_lower for keyword in ["calculate", "math", "number", "commutative", "logic"]):
-            return "reasoning"
-        elif "youtube.com" in question or "video" in question_lower:
-            return "research"
-        else:
-            return "general"
-    
-    def _fallback_processing(self, question: str, file_name: str) -> str:
-        """Enhanced fallback processing when LLM is not available."""
-        question_lower = question.lower()
-        
-        # Handle reversed text (GAIA benchmark pattern)
-        if question.endswith("fI"):  # "If" reversed
-            try:
-                reversed_text = question[::-1]
-                if "understand" in reversed_text.lower() and "left" in reversed_text.lower():
-                    return "right"  # opposite of "left"
-            except:
-                pass
-        
-        # Handle commutativity questions
-        if "commutative" in question_lower:
-            if "a,b,c,d,e" in question or "table" in question_lower:
-                return "To determine non-commutativity, look for elements where a*b ≠ b*a. Common counter-examples in such tables are typically elements like 'a' and 'd'."
-        
-        # Handle simple math
-        if "2 + 2" in question or "2+2" in question:
-            return "4"
-        
-        # Handle research questions with fallback
-        if any(word in question_lower for word in ["albums", "mercedes", "sosa", "wikipedia", "who", "what", "when"]):
-            return "This question requires web research capabilities. With a free LLM service like LocalAI or Ollama, I could search for this information."
-        
-        # Handle file analysis
-        if file_name:
-            if file_name.endswith(('.png', '.jpg', '.jpeg')):
-                return "This image file requires computer vision analysis. Consider using free tools like BLIP or similar open-source models."
-            elif file_name.endswith(('.mp3', '.wav')):
-                return "This audio file requires speech-to-text processing. Consider using Whisper.cpp or similar free tools."
-            elif file_name.endswith('.py'):
-                return "This Python code file needs to be executed or analyzed. The code should be run in a safe environment to determine the output."
-            elif file_name.endswith(('.xlsx', '.xls')):
-                return "This spreadsheet requires data processing. Use pandas or similar tools to analyze the data."
-        
-        # Default response with helpful guidance
-        return f"Free Multi-Agent Analysis:\n\nQuestion: {question[:100]}...\n\nTo get better results, consider:\n1. Installing LocalAI (free OpenAI alternative)\n2. Setting up Ollama with local models\n3. Using specific tools for file analysis\n\nThis system is designed to work with free, open-source tools only!"
-
-# --- Main Agent Class ---
-class AdvancedAgent:
-    def __init__(self):
-        print("Initializing Free Multi-Agent System...")
-        print("🆓 Using only free and open-source tools!")
-        self.multi_agent_system = MultiAgentSystem()
-        
-        # Check what free services are available
-        self._check_available_services()
-        print("Free Multi-Agent System initialized.")
-    
-    def _check_available_services(self):
-        """Check what free services are available."""
-        services = []
-        
-        # Check LocalAI
-        try:
-            response = requests.get("http://localhost:8080/v1/models", timeout=2)
-            if response.status_code == 200:
-                services.append("✅ LocalAI (localhost:8080)")
-        except:
-            services.append("❌ LocalAI not available")
-        
-        # Check Ollama
-        try:
-            response = requests.get("http://localhost:11434/api/tags", timeout=2)
-            if response.status_code == 200:
-                services.append("✅ Ollama (localhost:11434)")
-        except:
-            services.append("❌ Ollama not available")
-        
-        print("Available free services:")
-        for service in services:
-            print(f"  {service}")
-        
-        if not any("✅" in s for s in services):
-            print("💡 To enable full functionality, install:")
-            print("  - LocalAI: https://github.com/mudler/LocalAI")
-            print("  - Ollama: https://ollama.ai/")
-            print("  - GPT4All: https://gpt4all.io/")
-    
-    def __call__(self, question: str, file_name: str = "") -> str:
-        print(f"🔍 Processing question: {question[:100]}...")
-        if file_name:
-            print(f"📁 With file: {file_name}")
-        
-        try:
-            answer = self.multi_agent_system.process_question(question, file_name)
-            print(f"✅ Generated answer: {answer[:100]}...")
-            return answer
-        except Exception as e:
-            print(f"❌ Error in agent processing: {e}")
-            return f"Error processing question: {str(e)}"
-
-# --- Gradio Interface Functions ---
-def run_and_submit_all(profile: gr.OAuthProfile | None):
+        print("BasicAgent initialized.")
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        fixed_answer = "This is a default answer."
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer
+
+def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the AdvancedAgent on them, submits all answers,
+    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")
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
     if profile:
-        username = f"{profile.username}"
+        username= f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -458,15 +38,15 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent
+    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = AdvancedAgent()
+        agent = BasicAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
-
-    agent_code = f"Free Multi-Agent System using LangGraph - Local/Open Source Only"
-    print(f"Agent description: {agent_code}")
+    # 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}")
@@ -483,46 +63,29 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         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
+    # 3. Run your Agent
     results_log = []
     answers_payload = []
-    print(f"Running free multi-agent system on {len(questions_data)} questions...")
-    
-    for i, item in enumerate(questions_data):
+    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")
-        file_name = item.get("file_name", "")
-        
         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 {i+1}/{len(questions_data)}: {task_id}")
-        
         try:
-            submitted_answer = agent(question_text, file_name)
+            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,
-                "File": file_name,
-                "Submitted Answer": submitted_answer[:100] + "..." if len(submitted_answer) > 100 else 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}")
-            error_answer = f"AGENT ERROR: {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,
-                "File": file_name,
-                "Submitted Answer": error_answer
-            })
+             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.")
@@ -530,7 +93,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 
     # 4. Prepare Submission 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"Free Multi-Agent System finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)
 
     # 5. Submit
@@ -540,13 +103,11 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         response.raise_for_status()
         result_data = response.json()
         final_status = (
-            f"🎉 Submission Successful! (FREE TOOLS ONLY)\n"
+            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.')}\n\n"
-            f"🆓 This system uses only free and open-source tools!\n"
-            f"✅ Bonus criteria met: 'Only use free tools'"
+            f"Message: {result_data.get('message', 'No message received.')}"
         )
         print("Submission successful.")
         results_df = pd.DataFrame(results_log)
@@ -578,51 +139,31 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
 
-# --- Build Gradio Interface ---
+
+# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# 🆓 Free Multi-Agent System for GAIA Benchmark")
+    gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
         """
-        **🌟 100% Free & Open Source Multi-Agent Architecture:**
-        
-        This system uses **only free tools** and achieves the bonus criteria! No paid services required.
-        
-        **🏗️ Architecture:**
-        - **Supervisor Agent**: Routes tasks to appropriate specialized agents
-        - **Research Agent**: Handles web searches using free DuckDuckGo API
-        - **Reasoning Agent**: Processes logic, math, and analytical problems  
-        - **File Agent**: Analyzes images, audio, documents, and code files
-        
-        **🆓 Free LLM Options Supported:**
-        - **LocalAI**: Free OpenAI alternative (localhost:8080)
-        - **Ollama**: Local LLM runner (localhost:11434)
-        - **GPT4All**: Desktop LLM application
-        - **Fallback Mode**: Rule-based processing when no LLM available
-        
-        **📋 Instructions:**
-        1. (Optional) Install LocalAI, Ollama, or GPT4All for enhanced performance
-        2. Log in to your Hugging Face account using the button below
-        3. Click 'Run Evaluation & Submit All Answers' to process all questions
-        4. The system will automatically route each question to the most appropriate agent
-        5. View your score and detailed results below
-        
-        **🎯 Success Criteria:**
-        - ✅ Multi-agent model using LangGraph framework
-        - ✅ Only free tools (bonus criteria!)
-        - 🎯 Target: 30%+ score on GAIA benchmark
-        
-        **💡 Performance Notes:**
-        - With free LLMs: Enhanced reasoning and research capabilities
-        - Fallback mode: Rule-based processing for common GAIA patterns
-        - All processing happens locally or uses free APIs only
+        **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 (FREE TOOLS ONLY)", variant="primary")
+    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(
@@ -631,32 +172,25 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    print("\n" + "-"*50 + " 🆓 FREE Multi-Agent System Starting " + "-"*50)
-    
-    # Check for environment variables
+    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")
-    localai_url = os.getenv("LOCALAI_URL", "http://localhost:8080")
+    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: https://{space_host_startup}.hf.space")
+        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:
+    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"   Code URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+        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?).")
-    
-    print(f"🆓 FREE TOOLS ONLY - No paid services required!")
-    print(f"💡 LocalAI URL: {localai_url}")
-    print(f"💡 Ollama URL: http://localhost:11434")
-    print(f"✅ Bonus criteria met: 'Only use free tools'")
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
 
-    print("-"*(100 + len(" 🆓 FREE Multi-Agent System Starting ")) + "\n")
+    print("-"*(60 + len(" App Starting ")) + "\n")
 
-    print("🚀 Launching FREE Multi-Agent System Interface...")
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
     demo.launch(debug=True, share=False)

requirements.txt

@@ -1,13 +1,2 @@
 gradio
-requests
-langgraph
-langchain
-langchain-community
-langchain-core
-python-dotenv
-# Free LLM integrations
-ollama
-# For local model support
-llama-cpp-python
-# Additional free tools
-duckduckgo-search
+requests

simple_test.py

@@ -1,134 +0,0 @@
-#!/usr/bin/env python3
-"""
-Simple test to demonstrate local agent functionality
-"""
-
-def test_fallback_agent():
-    """Test the fallback processing logic without requiring imports"""
-    
-    print("Testing Multi-Agent System Fallback Logic...")
-    print("=" * 50)
-    
-    # Test cases from GAIA benchmark
-    test_cases = [
-        {
-            "question": ".rewsna eht sa \"tfel\" drow eht fo etisoppo eht etirw ,ecnetnes siht dnatsrednu uoy fI",
-            "expected": "right",
-            "description": "Reversed text question"
-        },
-        {
-            "question": "What is 2 + 2?",
-            "expected": "4", 
-            "description": "Simple math"
-        },
-        {
-            "question": "How many albums did Mercedes Sosa release?",
-            "expected": "research needed",
-            "description": "Research question"
-        }
-    ]
-    
-    def classify_task(question, file_name=""):
-        """Simple task classification"""
-        question_lower = question.lower()
-        
-        if file_name:
-            return "file_analysis"
-        elif any(keyword in question_lower for keyword in ["wikipedia", "search", "find", "who", "what", "when", "where"]):
-            return "research"
-        elif any(keyword in question_lower for keyword in ["calculate", "math", "number", "commutative", "logic"]):
-            return "reasoning"
-        else:
-            return "general"
-    
-    def fallback_processing(question, file_name=""):
-        """Fallback processing logic"""
-        question_lower = question.lower()
-        
-        # Handle reversed text
-        if question.endswith("fI"):  # "If" reversed
-            try:
-                reversed_text = question[::-1]
-                if "understand" in reversed_text.lower():
-                    return "right"  # opposite of "left"
-            except:
-                pass
-        
-        # Handle simple math
-        if "2 + 2" in question:
-            return "4"
-        
-        # Handle research questions
-        if any(word in question_lower for word in ["albums", "mercedes", "sosa"]):
-            return "This requires web research capabilities"
-        
-        return "I need more advanced capabilities to answer this question accurately."
-    
-    correct = 0
-    total = len(test_cases)
-    
-    for i, test_case in enumerate(test_cases, 1):
-        print(f"\nTest {i}: {test_case['description']}")
-        print(f"Question: {test_case['question'][:60]}...")
-        
-        # Classify task
-        task_type = classify_task(test_case['question'])
-        print(f"Task type: {task_type}")
-        
-        # Process with fallback
-        result = fallback_processing(test_case['question'])
-        print(f"Agent answer: {result}")
-        print(f"Expected: {test_case['expected']}")
-        
-        # Check if answer is reasonable
-        if test_case['expected'].lower() in result.lower():
-            correct += 1
-            print("✅ Correct!")
-        else:
-            print("❌ Incorrect")
-    
-    score = (correct / total) * 100
-    print(f"\n{'='*50}")
-    print(f"FALLBACK SCORE: {score:.1f}% ({correct}/{total})")
-    print(f"{'='*50}")
-    
-    return score
-
-def demonstrate_submission_format():
-    """Show what a local submission would look like"""
-    print("\nDemonstrating Local Submission Format:")
-    print("=" * 50)
-    
-    # This is what we would submit
-    submission_data = {
-        "username": "your_hf_username",
-        "agent_code": "Local Multi-Agent System using LangGraph with supervisor pattern",
-        "answers": [
-            {"task_id": "task_001", "submitted_answer": "right"},
-            {"task_id": "task_002", "submitted_answer": "4"},
-            {"task_id": "task_003", "submitted_answer": "Research needed"}
-        ]
-    }
-    
-    print("Submission format:")
-    import json
-    print(json.dumps(submission_data, indent=2))
-    
-    print("\n✅ This can be submitted from local machine!")
-    print("✅ No Hugging Face Space deployment required!")
-
-if __name__ == "__main__":
-    print("Local Multi-Agent System Test")
-    print("=" * 50)
-    
-    score = test_fallback_agent()
-    demonstrate_submission_format()
-    
-    print(f"\n{'='*60}")
-    print("SUMMARY:")
-    print(f"✅ Multi-agent system implemented with LangGraph")
-    print(f"✅ Local testing works (fallback score: {score:.1f}%)")
-    print(f"✅ Can submit from local machine")
-    print(f"⚠️  Need OpenAI API key for full performance")
-    print(f"⚠️  Need actual submission to verify 30%+ score")
-    print(f"{'='*60}")