some updates

README.md

@@ -1,8 +1,8 @@
 ---
-title: Template Final Assignment
-emoji: 🕵🏻‍♂️
-colorFrom: indigo
-colorTo: indigo
+title: GAIA Agent - Q&A Chatbot
+emoji: 🤖
+colorFrom: blue
+colorTo: purple
 sdk: gradio
 sdk_version: 5.25.2
 app_file: app.py
@@ -12,58 +12,166 @@ hf_oauth: true
 hf_oauth_expiration_minutes: 480
 ---
 
-# **GAIA Agent**
+# 🤖 **GAIA Agent - Advanced Q&A Chatbot**
 
-## **Introduction**
+## 🌟 **Introduction**
 
-**GAIA Agent** is an automated system built to tackle and submit solutions for the GAIA benchmark, which tests the capabilities of general-purpose AI agents on diverse and challenging tasks. These tasks require a combination of reasoning, code execution, information retrieval, data interpretation, and multimodal understanding. Powered by advanced language models (such as HuggingFace, and Groq), the agent incorporates a versatile set of tools including browser tools, code interpreter tools, mathematical tools, document processing tools, image processing and generation tools. It is designed for seamless interaction with the benchmark, offering automatic evaluation, submission, and result display through a user-friendly Gradio interface.
+**GAIA Agent** is a sophisticated AI-powered chatbot system designed to handle complex questions and tasks through an intuitive Q&A interface. Built on top of the GAIA benchmark framework, this agent combines advanced reasoning, code execution, web search, document processing, and multimodal understanding capabilities. The system features both a user-friendly chatbot interface and a comprehensive evaluation runner for benchmark testing.
 
-## **Tools Implementation**
+## 🚀 **Key Features**
 
-### **Browser tools** 
-- **Wikipedia Search:** Search Wikipedia for a query and return maximum 2 results.
-- **Web Search:** Search the web for a query and return maximum 2 results.
-- **Arxiv Search:** Search arXiv for a query and return maximum 2 results.
+- **🔍 Multi-Modal Search**: Web search, Wikipedia, and arXiv paper search
+- **💻 Code Execution**: Support for Python, Bash, SQL, C, and Java
+- **🖼️ Image Processing**: Analysis, transformation, OCR, and generation
+- **📄 Document Processing**: PDF, CSV, Excel, and text file analysis
+- **📁 File Upload Support**: Handle multiple file types with drag-and-drop
+- **🧮 Mathematical Operations**: Complete set of mathematical tools
+- **💬 Conversational Interface**: Natural chat-based interaction
+- **📊 Evaluation System**: Automated benchmark testing and submission
 
-### **Code interpreter tools**
-- **Execute Multi-programming Language:** Execute code in multiple languages (Python, Bash, SQL, C, Java) and return results.
+## 🏗️ **Project Structure**
 
-### **Mathematical tools**
-- **Multiplication Tools:** Multiplies 2 numbers 
-- **Addition:** Adds 2 numbers
-- **Subtraction:** Subtracts 2 numbers 
-- **Division:** Divides 2 numbers 
-- **Modulus:** Get the modulus of 2 numbers
-- **Power:** Get the power of 2 numbers 
-- **Square root:** Get the square root of a number
+```
+gaia-agent/
+├── app.py                    # Main Q&A chatbot interface
+├── evaluation_app.py         # GAIA benchmark evaluation runner
+├── agent.py                  # Core agent implementation with tools
+├── code_interpreter.py       # Multi-language code execution
+├── image_processing.py       # Image processing utilities
+├── system_prompt.txt         # System prompt for the agent
+├── requirements.txt          # Python dependencies
+├── metadata.jsonl           # GAIA benchmark metadata
+├── explore_metadata.ipynb   # Data exploration notebook
+└── README.md               # This file
+```
 
-### **Document processing tools**
-- **Save and Read File:** Save content to a file and return the path 
-- **Download a File from URL:** Download a file from a URL and save it to a temporary location
-- **Extract Text from Image:** Extract text from an image using OCR library pytesseract (if available)
-- **Analyze CSV File:** Analyze a CSV file using pandas and answer a question about it 
-- **Analyze Excel File:** Analyze an Excel file using pandas and answer a question about it
+## 🛠️ **Tool Categories**
+
+### **🌐 Browser & Search Tools**
+- **Wikipedia Search**: Search Wikipedia with up to 2 results
+- **Web Search**: Tavily-powered web search with up to 3 results  
+- **arXiv Search**: Academic paper search with up to 3 results
+
+### **💻 Code Interpreter Tools**
+- **Multi-Language Execution**: Python, Bash, SQL, C, Java support
+- **Plot Generation**: Matplotlib visualization support
+- **DataFrame Analysis**: Pandas data processing
+- **Error Handling**: Comprehensive error reporting
+
+### **🧮 Mathematical Tools**
+- **Basic Operations**: Add, subtract, multiply, divide
+- **Advanced Functions**: Modulus, power, square root
+- **Complex Numbers**: Support for complex number operations
+
+### **📄 Document Processing Tools**
+- **File Operations**: Save, read, and download files
+- **CSV Analysis**: Pandas-based data analysis
+- **Excel Processing**: Excel file analysis and processing
+- **OCR**: Extract text from images using Tesseract
+
+### **🖼️ Image Processing & Generation Tools**
+- **Image Analysis**: Size, color, and property analysis
+- **Transformations**: Resize, rotate, crop, flip, adjust brightness/contrast
+- **Drawing Tools**: Add shapes, text, and annotations
+- **Image Generation**: Create gradients, noise patterns, and simple graphics
+- **Image Combination**: Stack and combine multiple images
+
+## 🎯 **How to Use**
+
+### **Q&A Chatbot Interface (app.py)**
+
+1. **Start the Chatbot:**
+   ```bash
+   python app.py
+   ```
+
+2. **Access the Interface:**
+   - Open `http://localhost:7860` in your browser
+   - Upload files (images, documents, CSV, etc.) if needed
+   - Ask questions in natural language
+   - Get comprehensive answers with tool usage
+
+3. **Supported Interactions:**
+   - **Text Questions**: "What is the capital of France?"
+   - **Math Problems**: "Calculate the square root of 144"
+   - **Code Requests**: "Write a Python function to sort a list"
+   - **Image Analysis**: Upload an image and ask "What do you see?"
+   - **Data Analysis**: Upload a CSV and ask "What are the trends?"
+   - **Web Search**: "What are the latest AI developments?"
+
+### **Evaluation Runner (evaluation_app.py)**
+
+1. **Run the Evaluation:**
+   ```bash
+   python evaluation_app.py
+   ```
+
+2. **Benchmark Testing:**
+   - Log in with your Hugging Face account
+   - Click "Run Evaluation & Submit All Answers"
+   - Monitor progress as the agent processes GAIA benchmark questions
+   - View results and scores automatically
+
+## 🔧 **Technical Architecture**
+
+### **LangGraph State Machine**
+```
+START → Retriever → Assistant → Tools → Assistant
+                     ↑              ↓
+                     └──────────────┘
+```
 
-### **Image processing and generation tools**
-- **Analyze Image:** Analyze basic properties of an image (size, mode, color analysis, thumbnail preview)
-- **Transform Image:** Apply transformations: resize, rotate, crop, flip, brightness, contrast, blur, sharpen, grayscale
-- **Draw on Image:** Draw shapes (rectangle, circle, line) or text onto an image
-- **Generate Simple Image:** Generate a simple image (gradient, noise, pattern, chart)
-- **Combine Images:** Combine multiple images (collage, stack, blend)
+1. **Retriever Node**: Searches vector database for similar questions
+2. **Assistant Node**: LLM processes question with available tools
+3. **Tools Node**: Executes selected tools (web search, code, etc.)
+4. **Conditional Routing**: Dynamically routes between assistant and tools
 
-## **Database Setup**
+### **Vector Database Integration**
+- **Supabase Vector Store**: Stores GAIA benchmark Q&A pairs
+- **Semantic Search**: Finds similar questions for context
+- **HuggingFace Embeddings**: sentence-transformers/all-mpnet-base-v2
 
-### **Supabase Vector Store**
-This project uses Supabase as a vector database to store and retrieve GAIA benchmark questions and answers. The vector store enables semantic search capabilities for finding relevant examples.
+### **Multi-Modal File Support**
+- **Images**: JPG, PNG, GIF, BMP, WebP (Base64 processing)
+- **Documents**: PDF, DOC, DOCX, TXT, MD (Content extraction)
+- **Data**: CSV, Excel (Pandas analysis)
+- **Code**: Python, JavaScript, HTML, CSS, JSON, XML
 
-### **Creating the `match_documents_2` Function**
-To set up the vector similarity search function in your Supabase database, execute the following SQL commands:
+## ⚙️ **Installation & Setup**
 
+### **1. Clone Repository**
+```bash
+git clone https://github.com/fisherman611/gaia-agent.git
+cd gaia-agent
+```
+
+### **2. Install Dependencies**
+```bash
+pip install -r requirements.txt
+```
+
+### **3. Environment Variables**
+Create a `.env` file with your API keys:
+```env
+SUPABASE_URL=your_supabase_url
+SUPABASE_SERVICE_ROLE_KEY=your_supabase_key
+GROQ_API_KEY=your_groq_api_key
+TAVILY_API_KEY=your_tavily_api_key
+HUGGINGFACEHUB_API_TOKEN=your_hf_token
+LANGSMITH_API_KEY=your_langsmith_key
+
+LANGSMITH_TRACING=true
+LANGSMITH_PROJECT=ai_agent_course
+LANGSMITH_ENDPOINT=https://api.smith.langchain.com
+```
+
+### **4. Database Setup (Supabase)**
+Execute this SQL in your Supabase database:
 ```sql
--- 1. Make sure pgvector is enabled
+-- Enable pgvector extension
 CREATE EXTENSION IF NOT EXISTS vector;
 
--- 2. Create the RPC for your "documents2" table
+-- Create match function for documents2 table
 CREATE OR REPLACE FUNCTION public.match_documents_2(
   query_embedding vector(768)
 )
@@ -87,63 +195,42 @@ AS $$
   LIMIT 10;
 $$;
 
--- 3. Grant execute to anon/authenticated
-GRANT EXECUTE ON FUNCTION public.match_documents(vector) TO anon, authenticated;
+-- Grant permissions
+GRANT EXECUTE ON FUNCTION public.match_documents_2(vector) TO anon, authenticated;
 ```
 
-This setup:
-1. Enables the pgvector extension for vector operations
-2. Creates a stored function that performs vector similarity search using cosine distance (`<=>`)
-3. Grants execution permissions to anonymous and authenticated users
-4. Returns the top 10 most similar documents with their similarity scores
-
-## **Installation**
-Clone the repository, change the current working directory to this repository's root folder:
+## 🚀 **Running the Application**
 
+### **Chatbot Interface**
+```bash
+python app.py
 ```
-git clone https://github.com/fisherman611/gaia-agent.git
-```
-```
-cd gaia-agent
-```
-
-Install ```requirements.txt``` (replace `3.11` with your installed Python version):
+Access at: `http://localhost:7860`
 
+### **Evaluation Runner**
+```bash
+python evaluation_app.py
 ```
-py -3.11 -m pip install -r requirements.txt
-```
-
-## **Environment Variables**
-Store some API keys an variables in the `.env` file and load it in your code using `load_dotenv`
+Access at: `http://localhost:7860`
 
-```
-SUPABASE_URL=...
-SUPABASE_SERVICE_ROLE_KEY=...
-SUPABASE_SERVICE_KEY=...
-HUGGINGFACEHUB_API_TOKEN=...
-GROQ_API_KEY=...
-TAVILY_API_KEY=...
-LANGSMITH_API_KEY=...
+### **Live Demo**
+Try it online: [Hugging Face Space](https://huggingface.co/spaces/fisherman611/gaia-agent)
 
-LANGSMITH_TRACING=true
-LANGSMITH_PROJECT=ai_agent_course
-LANGSMITH_ENDPOINT=https://api.smith.langchain.com
-```
+## 🔗 **Resources**
 
-## **Demo**
-To run the application using the command line, use the following command (replace `3.11` with your installed Python version):
-```
-py -3.11 app.py
-```
-Or run in the [Hugging Face Space](https://huggingface.co/spaces/fisherman611/gaia-agent)
-## **Resources**
 - [GAIA Benchmark](https://huggingface.co/spaces/gaia-benchmark/leaderboard)
 - [Hugging Face Agents Course](https://huggingface.co/agents-course)
-- [Langgraph Agents](https://langchain-ai.github.io/langgraph/)
+- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/)
+- [Supabase Vector Store](https://supabase.com/docs/guides/ai/vector-columns)
+
+## 🤝 **Contributing**
 
+Contributions are welcome! Areas for improvement:
+- **New Tools**: Add specialized tools for specific domains
+- **UI Enhancements**: Improve the chatbot interface
+- **Performance**: Optimize response times and accuracy
+- **Documentation**: Expand examples and use cases
 
-## **Contributing**
-Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.
+## 📄 **License**
 
-## **License**
 This project is licensed under the [MIT License](https://mit-license.org/).

app.py

@@ -1,211 +1,364 @@
-""" Basic Agent Evaluation Runner"""
-import os
-import inspect
 import gradio as gr
-import requests
-import pandas as pd
 import time
+import os
+import base64
+from typing import List, Tuple, Optional
 from langchain_core.messages import HumanMessage
 from agent import build_graph
 
-
-
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-
-
-class BasicAgent:
-    """A langgraph agent."""
+class QnAChatbot:
+    """A Q&A chatbot interface for the agent."""
+    
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("🤖 QnAChatbot initializing...")
+        print("🔧 Building agent graph...")
         self.graph = build_graph()
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        # Wrap the question in a HumanMessage from langchain_core
-        messages = [HumanMessage(content=question)]
-        messages = self.graph.invoke({"messages": messages})
-        answer = messages['messages'][-1].content
-        return answer[14:]
-
-
-def run_and_submit_all( profile: gr.OAuthProfile | None):
-    """
-    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") # 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 ( modify this part to create your agent)
-    try:
-        agent = BasicAgent()
-    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 ( 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}")
-    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 your 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
-        
-        time.sleep(30)
+        self.conversation_history = []
+        print("✅ QnAChatbot initialized successfully")
+    
+    def process_question(self, question: str, history: List[Tuple[str, str]], uploaded_files: Optional[List] = None) -> Tuple[str, List[Tuple[str, str]]]:
+        """Process a question and return the response with updated history."""
+        if not question.strip() and not uploaded_files:
+            print("⚠️  No question or files provided")
+            return "", history
         
         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"\n{'='*60}")
+            print(f"🤖 Processing new question...")
+            print(f"📝 Question: {question[:100]}{'...' if len(question) > 100 else ''}")
+            print(f"📁 Files uploaded: {len(uploaded_files) if uploaded_files else 0}")
+            
+            # Handle uploaded files
+            file_context = ""
+            if uploaded_files:
+                print(f"📂 Processing {len(uploaded_files)} uploaded file(s)...")
+                file_context = self._process_uploaded_files(uploaded_files)
+                if file_context:
+                    original_question = question
+                    question = f"{question}\n\n{file_context}" if question.strip() else file_context
+                    print(f"📋 File context added to question (length: {len(file_context)} chars)")
+            
+            # Wrap the question in a HumanMessage
+            messages = [HumanMessage(content=question)]
+            print(f"🔄 Invoking agent graph...")
+            
+            # Get response from the agent
+            result = self.graph.invoke({"messages": messages})
+            print(f"📨 Received {len(result['messages'])} message(s) from agent")
+            
+            # Print all messages for debugging
+            for i, msg in enumerate(result['messages']):
+                print(f"📧 Message {i+1}: {type(msg).__name__}")
+                if hasattr(msg, 'content'):
+                    content_preview = msg.content[:200] + "..." if len(msg.content) > 200 else msg.content
+                    print(f"   Content preview: {content_preview}")
+            
+            answer = result['messages'][-1].content
+            
+            # Clean up the answer if it starts with "Assistant: "
+            if answer.startswith("Assistant: "):
+                answer = answer[11:]
+                print("🧹 Cleaned 'Assistant: ' prefix from response")
+            
+            # Update conversation history
+            history.append((question, answer))
+            print(f"✅ Question processed successfully")
+            print(f"📊 Response length: {len(answer)} characters")
+            print(f"💬 Total conversation history: {len(history)} exchanges")
+            print(f"{'='*60}\n")
+            
+            return "", history
+            
         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)
+            error_msg = f"Error processing question: {str(e)}"
+            print(f"❌ {error_msg}")
+            print(f"🔍 Exception details: {type(e).__name__}: {str(e)}")
+            import traceback
+            print(f"📋 Traceback:\n{traceback.format_exc()}")
+            history.append((question, error_msg))
+            print(f"{'='*60}\n")
+            return "", history
+    
+    def _process_uploaded_files(self, uploaded_files: List) -> str:
+        """Process uploaded files and return context for the question."""
+        file_contexts = []
+        
+        for file_path in uploaded_files:
+            if not file_path or not os.path.exists(file_path):
+                print(f"⚠️  Skipping invalid file path: {file_path}")
+                continue
+                
+            try:
+                file_name = os.path.basename(file_path)
+                file_ext = os.path.splitext(file_name)[1].lower()
+                file_size = os.path.getsize(file_path)
+                
+                print(f"📄 Processing file: {file_name} ({file_size} bytes, {file_ext})")
+                
+                # Handle different file types
+                if file_ext in ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp']:
+                    # Image file - convert to base64
+                    with open(file_path, 'rb') as f:
+                        image_data = base64.b64encode(f.read()).decode('utf-8')
+                    file_contexts.append(f"[UPLOADED IMAGE: {file_name}] - Base64 data: {image_data}")
+                    print(f"🖼️  Image converted to base64 ({len(image_data)} chars)")
+                    
+                elif file_ext in ['.txt', '.md', '.py', '.js', '.html', '.css', '.json', '.xml']:
+                    # Text file - read content
+                    with open(file_path, 'r', encoding='utf-8') as f:
+                        content = f.read()
+                    file_contexts.append(f"[UPLOADED TEXT FILE: {file_name}]\nContent:\n{content}")
+                    print(f"📝 Text file content read ({len(content)} chars)")
+                    
+                elif file_ext in ['.csv']:
+                    # CSV file - provide file path for analysis
+                    file_contexts.append(f"[UPLOADED CSV FILE: {file_name}] - File path: {file_path}")
+                    print(f"📊 CSV file prepared for analysis")
+                    
+                elif file_ext in ['.xlsx', '.xls']:
+                    # Excel file - provide file path for analysis
+                    file_contexts.append(f"[UPLOADED EXCEL FILE: {file_name}] - File path: {file_path}")
+                    print(f"📈 Excel file prepared for analysis")
+                    
+                elif file_ext in ['.pdf']:
+                    # PDF file - mention it's available
+                    file_contexts.append(f"[UPLOADED PDF FILE: {file_name}] - File path: {file_path}")
+                    print(f"📄 PDF file prepared for processing")
+                    
+                else:
+                    # Other file types - just mention the file
+                    file_contexts.append(f"[UPLOADED FILE: {file_name}] - File path: {file_path}")
+                    print(f"📁 Generic file prepared for processing")
+                    
+            except Exception as e:
+                error_msg = f"Error processing file {file_path}: {e}"
+                print(f"❌ {error_msg}")
+                print(f"🔍 File processing error details: {type(e).__name__}: {str(e)}")
+                file_contexts.append(f"[ERROR PROCESSING FILE: {os.path.basename(file_path)}] - {str(e)}")
+        
+        total_context = "\n\n".join(file_contexts) if file_contexts else ""
+        if total_context:
+            print(f"📋 Total file context generated: {len(total_context)} characters")
+        
+        return total_context
+    
+    def clear_history(self):
+        """Clear the conversation history."""
+        print("🧹 Clearing conversation history...")
+        self.conversation_history = []
+        print("✅ Conversation history cleared")
+        return []
 
-    # 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.')}"
+def create_qna_interface():
+    """Create the Q&A chatbot interface."""
+    
+    print("🚀 Creating Q&A interface...")
+    # Initialize the chatbot
+    chatbot = QnAChatbot()
+    print("🎨 Setting up UI components...")
+    
+    # Custom CSS for better styling
+    custom_css = """
+    .gradio-container {
+        max-width: 1200px !important;
+        margin: auto !important;
+    }
+    .chat-message {
+        padding: 10px !important;
+        margin: 5px 0 !important;
+        border-radius: 10px !important;
+    }
+    .user-message {
+        background-color: #e3f2fd !important;
+        margin-left: 20% !important;
+    }
+    .bot-message {
+        background-color: #f5f5f5 !important;
+        margin-right: 20% !important;
+    }
+    """
+    
+    with gr.Blocks(css=custom_css, title="GAIA Agent - Q&A Chatbot") as demo:
+        gr.Markdown(
+            """
+            # 🤖 GAIA Agent - Q&A Chatbot
+            
+            Welcome to the GAIA Agent Q&A interface! Ask me anything and I'll help you find the answer using my various tools and capabilities.
+            
+            **What I can do:**
+            - 🔍 Search the web, Wikipedia, and academic papers
+            - 🧮 Perform mathematical calculations
+            - 💻 Execute code in multiple languages (Python, Bash, SQL, C, Java)
+            - 📊 Analyze CSV and Excel files
+            - 🖼️ Process and analyze images (JPG, PNG, GIF, etc.)
+            - 📄 Extract text from images (OCR)
+            - 📁 Handle file uploads and processing (PDF, DOC, TXT, etc.)
+            - 📈 Create visualizations and charts
+            - 🔧 Multi-file analysis and comparison
+            - And much more!
+            
+            ---
+            """
         )
-        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("# Basic 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 seperate 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)
-    # Removed max_rows=10 from DataFrame constructor
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+        
+        # Chat interface
+        with gr.Row():
+            with gr.Column(scale=1):
+                chatbot_interface = gr.Chatbot(
+                    label="Conversation",
+                    height=500,
+                    show_label=True,
+                    container=True,
+                    bubble_full_width=False
+                )
+        
+        # File upload section
+        with gr.Row():
+            with gr.Column():
+                file_upload = gr.File(
+                    label="📁 Upload Files (Images, Documents, CSV, Excel, etc.)",
+                    file_count="multiple",
+                    file_types=[
+                        ".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp",  # Images
+                        ".txt", ".md", ".py", ".js", ".html", ".css", ".json", ".xml",  # Text files
+                        ".csv", ".xlsx", ".xls",  # Data files
+                        ".pdf", ".doc", ".docx"  # Documents
+                    ],
+                    height=100
+                )
+        
+        with gr.Row():
+            with gr.Column(scale=8):
+                question_input = gr.Textbox(
+                    label="Ask a question",
+                    placeholder="Type your question here or upload files above... (e.g., 'What is the capital of France?', 'Analyze this image', 'Summarize this document')",
+                    lines=2,
+                    max_lines=5
+                )
+            with gr.Column(scale=1, min_width=100):
+                submit_btn = gr.Button("Send", variant="primary", size="lg")
+        
+        with gr.Row():
+            clear_btn = gr.Button("Clear History", variant="secondary")
+            clear_files_btn = gr.Button("Clear Files", variant="secondary")
+            
+        # Example questions
+        with gr.Row():
+            gr.Markdown("### 💡 Example Questions:")
+            
+        with gr.Row():
+            with gr.Column():
+                gr.Examples(
+                    examples=[
+                        "What is the current population of Tokyo?",
+                        "Calculate the square root of 144",
+                        "Write a Python function to sort a list",
+                        "What are the latest developments in AI?",
+                        "Explain quantum computing in simple terms",
+                    ],
+                    inputs=question_input,
+                    label="General Questions"
+                )
+            with gr.Column():
+                gr.Examples(
+                    examples=[
+                        "Search for recent papers on machine learning",
+                        "What is the weather like today?",
+                        "Create a simple bar chart using Python",
+                        "Convert 100 USD to EUR",
+                        "What are the benefits of renewable energy?",
+                    ],
+                    inputs=question_input,
+                    label="Research & Analysis"
+                )
+            with gr.Column():
+                gr.Examples(
+                    examples=[
+                        "Analyze this image and describe what you see",
+                        "Extract text from this image using OCR",
+                        "Summarize the content of this document",
+                        "Analyze the data in this CSV file",
+                        "What insights can you find in this Excel file?",
+                    ],
+                    inputs=question_input,
+                    label="File Analysis"
+                )
+        
+        # Event handlers
+        def submit_question(question, history, files):
+            print(f"🎯 UI: Submit button clicked")
+            print(f"📝 UI: Question length: {len(question) if question else 0}")
+            print(f"📁 UI: Files count: {len(files) if files else 0}")
+            result_question, result_history = chatbot.process_question(question, history, files)
+            print(f"🔄 UI: Returning results and clearing files")
+            return result_question, result_history, None  # Clear files after processing
+        
+        def clear_conversation():
+            print("🧹 UI: Clear conversation button clicked")
+            return chatbot.clear_history()
+        
+        def clear_files():
+            print("🗑️  UI: Clear files button clicked")
+            return None
+        
+        # Connect the events
+        submit_btn.click(
+            fn=submit_question,
+            inputs=[question_input, chatbot_interface, file_upload],
+            outputs=[question_input, chatbot_interface, file_upload],
+            show_progress=True
+        )
+        
+        question_input.submit(
+            fn=submit_question,
+            inputs=[question_input, chatbot_interface, file_upload],
+            outputs=[question_input, chatbot_interface, file_upload],
+            show_progress=True
+        )
+        
+        clear_btn.click(
+            fn=clear_conversation,
+            outputs=[chatbot_interface],
+            show_progress=False
+        )
+        
+        clear_files_btn.click(
+            fn=clear_files,
+            outputs=[file_upload],
+            show_progress=False
+        )
+        
+        # Footer
+        gr.Markdown(
+            """
+            ---
+            
+            **Note:** This agent uses various tools and APIs to provide comprehensive answers. 
+            Processing complex questions and file analysis may take some time. Please be patient!
+            
+            **Supported file types:** 
+            - **Images:** JPG, PNG, GIF, BMP, WebP
+            - **Documents:** PDF, DOC, DOCX, TXT, MD
+            - **Data files:** CSV, Excel (XLS, XLSX)
+            - **Code files:** Python, JavaScript, HTML, CSS, JSON, XML
+            
+            **Powered by:** LangGraph, Groq, and various specialized tools
+            """
+        )
+    
+    return demo
 
 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 Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    print("\n" + "-"*50)
+    print("🚀 Starting GAIA Agent Q&A Chatbot...")
+    print("-"*50 + "\n")
+    
+    # Create and launch the interface
+    demo = create_qna_interface()
+    demo.launch(
+        debug=True,
+        share=False,
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True
+    ) 

evaluation_app.py

@@ -0,0 +1,211 @@
+""" Basic Agent Evaluation Runner"""
+import os
+import inspect
+import gradio as gr
+import requests
+import pandas as pd
+import time
+from langchain_core.messages import HumanMessage
+from agent import build_graph
+
+
+
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+
+class BasicAgent:
+    """A langgraph agent."""
+    def __init__(self):
+        print("BasicAgent initialized.")
+        self.graph = build_graph()
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        # Wrap the question in a HumanMessage from langchain_core
+        messages = [HumanMessage(content=question)]
+        messages = self.graph.invoke({"messages": messages})
+        answer = messages['messages'][-1].content
+        return answer[14:]
+
+
+def run_and_submit_all( profile: gr.OAuthProfile | None):
+    """
+    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") # 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 ( modify this part to create your agent)
+    try:
+        agent = BasicAgent()
+    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 ( 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}")
+    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 your 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
+        
+        time.sleep(30)
+        
+        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})
+        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("# Basic 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 seperate 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)
+    # Removed max_rows=10 from DataFrame constructor
+    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 Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)

qna_ui.py

@@ -1,364 +0,0 @@
-import gradio as gr
-import time
-import os
-import base64
-from typing import List, Tuple, Optional
-from langchain_core.messages import HumanMessage
-from agent import build_graph
-
-class QnAChatbot:
-    """A Q&A chatbot interface for the agent."""
-    
-    def __init__(self):
-        print("🤖 QnAChatbot initializing...")
-        print("🔧 Building agent graph...")
-        self.graph = build_graph()
-        self.conversation_history = []
-        print("✅ QnAChatbot initialized successfully")
-    
-    def process_question(self, question: str, history: List[Tuple[str, str]], uploaded_files: Optional[List] = None) -> Tuple[str, List[Tuple[str, str]]]:
-        """Process a question and return the response with updated history."""
-        if not question.strip() and not uploaded_files:
-            print("⚠️  No question or files provided")
-            return "", history
-        
-        try:
-            print(f"\n{'='*60}")
-            print(f"🤖 Processing new question...")
-            print(f"📝 Question: {question[:100]}{'...' if len(question) > 100 else ''}")
-            print(f"📁 Files uploaded: {len(uploaded_files) if uploaded_files else 0}")
-            
-            # Handle uploaded files
-            file_context = ""
-            if uploaded_files:
-                print(f"📂 Processing {len(uploaded_files)} uploaded file(s)...")
-                file_context = self._process_uploaded_files(uploaded_files)
-                if file_context:
-                    original_question = question
-                    question = f"{question}\n\n{file_context}" if question.strip() else file_context
-                    print(f"📋 File context added to question (length: {len(file_context)} chars)")
-            
-            # Wrap the question in a HumanMessage
-            messages = [HumanMessage(content=question)]
-            print(f"🔄 Invoking agent graph...")
-            
-            # Get response from the agent
-            result = self.graph.invoke({"messages": messages})
-            print(f"📨 Received {len(result['messages'])} message(s) from agent")
-            
-            # Print all messages for debugging
-            for i, msg in enumerate(result['messages']):
-                print(f"📧 Message {i+1}: {type(msg).__name__}")
-                if hasattr(msg, 'content'):
-                    content_preview = msg.content[:200] + "..." if len(msg.content) > 200 else msg.content
-                    print(f"   Content preview: {content_preview}")
-            
-            answer = result['messages'][-1].content
-            
-            # Clean up the answer if it starts with "Assistant: "
-            if answer.startswith("Assistant: "):
-                answer = answer[11:]
-                print("🧹 Cleaned 'Assistant: ' prefix from response")
-            
-            # Update conversation history
-            history.append((question, answer))
-            print(f"✅ Question processed successfully")
-            print(f"📊 Response length: {len(answer)} characters")
-            print(f"💬 Total conversation history: {len(history)} exchanges")
-            print(f"{'='*60}\n")
-            
-            return "", history
-            
-        except Exception as e:
-            error_msg = f"Error processing question: {str(e)}"
-            print(f"❌ {error_msg}")
-            print(f"🔍 Exception details: {type(e).__name__}: {str(e)}")
-            import traceback
-            print(f"📋 Traceback:\n{traceback.format_exc()}")
-            history.append((question, error_msg))
-            print(f"{'='*60}\n")
-            return "", history
-    
-    def _process_uploaded_files(self, uploaded_files: List) -> str:
-        """Process uploaded files and return context for the question."""
-        file_contexts = []
-        
-        for file_path in uploaded_files:
-            if not file_path or not os.path.exists(file_path):
-                print(f"⚠️  Skipping invalid file path: {file_path}")
-                continue
-                
-            try:
-                file_name = os.path.basename(file_path)
-                file_ext = os.path.splitext(file_name)[1].lower()
-                file_size = os.path.getsize(file_path)
-                
-                print(f"📄 Processing file: {file_name} ({file_size} bytes, {file_ext})")
-                
-                # Handle different file types
-                if file_ext in ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp']:
-                    # Image file - convert to base64
-                    with open(file_path, 'rb') as f:
-                        image_data = base64.b64encode(f.read()).decode('utf-8')
-                    file_contexts.append(f"[UPLOADED IMAGE: {file_name}] - Base64 data: {image_data}")
-                    print(f"🖼️  Image converted to base64 ({len(image_data)} chars)")
-                    
-                elif file_ext in ['.txt', '.md', '.py', '.js', '.html', '.css', '.json', '.xml']:
-                    # Text file - read content
-                    with open(file_path, 'r', encoding='utf-8') as f:
-                        content = f.read()
-                    file_contexts.append(f"[UPLOADED TEXT FILE: {file_name}]\nContent:\n{content}")
-                    print(f"📝 Text file content read ({len(content)} chars)")
-                    
-                elif file_ext in ['.csv']:
-                    # CSV file - provide file path for analysis
-                    file_contexts.append(f"[UPLOADED CSV FILE: {file_name}] - File path: {file_path}")
-                    print(f"📊 CSV file prepared for analysis")
-                    
-                elif file_ext in ['.xlsx', '.xls']:
-                    # Excel file - provide file path for analysis
-                    file_contexts.append(f"[UPLOADED EXCEL FILE: {file_name}] - File path: {file_path}")
-                    print(f"📈 Excel file prepared for analysis")
-                    
-                elif file_ext in ['.pdf']:
-                    # PDF file - mention it's available
-                    file_contexts.append(f"[UPLOADED PDF FILE: {file_name}] - File path: {file_path}")
-                    print(f"📄 PDF file prepared for processing")
-                    
-                else:
-                    # Other file types - just mention the file
-                    file_contexts.append(f"[UPLOADED FILE: {file_name}] - File path: {file_path}")
-                    print(f"📁 Generic file prepared for processing")
-                    
-            except Exception as e:
-                error_msg = f"Error processing file {file_path}: {e}"
-                print(f"❌ {error_msg}")
-                print(f"🔍 File processing error details: {type(e).__name__}: {str(e)}")
-                file_contexts.append(f"[ERROR PROCESSING FILE: {os.path.basename(file_path)}] - {str(e)}")
-        
-        total_context = "\n\n".join(file_contexts) if file_contexts else ""
-        if total_context:
-            print(f"📋 Total file context generated: {len(total_context)} characters")
-        
-        return total_context
-    
-    def clear_history(self):
-        """Clear the conversation history."""
-        print("🧹 Clearing conversation history...")
-        self.conversation_history = []
-        print("✅ Conversation history cleared")
-        return []
-
-def create_qna_interface():
-    """Create the Q&A chatbot interface."""
-    
-    print("🚀 Creating Q&A interface...")
-    # Initialize the chatbot
-    chatbot = QnAChatbot()
-    print("🎨 Setting up UI components...")
-    
-    # Custom CSS for better styling
-    custom_css = """
-    .gradio-container {
-        max-width: 1200px !important;
-        margin: auto !important;
-    }
-    .chat-message {
-        padding: 10px !important;
-        margin: 5px 0 !important;
-        border-radius: 10px !important;
-    }
-    .user-message {
-        background-color: #e3f2fd !important;
-        margin-left: 20% !important;
-    }
-    .bot-message {
-        background-color: #f5f5f5 !important;
-        margin-right: 20% !important;
-    }
-    """
-    
-    with gr.Blocks(css=custom_css, title="GAIA Agent - Q&A Chatbot") as demo:
-        gr.Markdown(
-            """
-            # 🤖 GAIA Agent - Q&A Chatbot
-            
-            Welcome to the GAIA Agent Q&A interface! Ask me anything and I'll help you find the answer using my various tools and capabilities.
-            
-            **What I can do:**
-            - 🔍 Search the web, Wikipedia, and academic papers
-            - 🧮 Perform mathematical calculations
-            - 💻 Execute code in multiple languages (Python, Bash, SQL, C, Java)
-            - 📊 Analyze CSV and Excel files
-            - 🖼️ Process and analyze images (JPG, PNG, GIF, etc.)
-            - 📄 Extract text from images (OCR)
-            - 📁 Handle file uploads and processing (PDF, DOC, TXT, etc.)
-            - 📈 Create visualizations and charts
-            - 🔧 Multi-file analysis and comparison
-            - And much more!
-            
-            ---
-            """
-        )
-        
-        # Chat interface
-        with gr.Row():
-            with gr.Column(scale=1):
-                chatbot_interface = gr.Chatbot(
-                    label="Conversation",
-                    height=500,
-                    show_label=True,
-                    container=True,
-                    bubble_full_width=False
-                )
-        
-        # File upload section
-        with gr.Row():
-            with gr.Column():
-                file_upload = gr.File(
-                    label="📁 Upload Files (Images, Documents, CSV, Excel, etc.)",
-                    file_count="multiple",
-                    file_types=[
-                        ".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp",  # Images
-                        ".txt", ".md", ".py", ".js", ".html", ".css", ".json", ".xml",  # Text files
-                        ".csv", ".xlsx", ".xls",  # Data files
-                        ".pdf", ".doc", ".docx"  # Documents
-                    ],
-                    height=100
-                )
-        
-        with gr.Row():
-            with gr.Column(scale=8):
-                question_input = gr.Textbox(
-                    label="Ask a question",
-                    placeholder="Type your question here or upload files above... (e.g., 'What is the capital of France?', 'Analyze this image', 'Summarize this document')",
-                    lines=2,
-                    max_lines=5
-                )
-            with gr.Column(scale=1, min_width=100):
-                submit_btn = gr.Button("Send", variant="primary", size="lg")
-        
-        with gr.Row():
-            clear_btn = gr.Button("Clear History", variant="secondary")
-            clear_files_btn = gr.Button("Clear Files", variant="secondary")
-            
-        # Example questions
-        with gr.Row():
-            gr.Markdown("### 💡 Example Questions:")
-            
-        with gr.Row():
-            with gr.Column():
-                gr.Examples(
-                    examples=[
-                        "What is the current population of Tokyo?",
-                        "Calculate the square root of 144",
-                        "Write a Python function to sort a list",
-                        "What are the latest developments in AI?",
-                        "Explain quantum computing in simple terms",
-                    ],
-                    inputs=question_input,
-                    label="General Questions"
-                )
-            with gr.Column():
-                gr.Examples(
-                    examples=[
-                        "Search for recent papers on machine learning",
-                        "What is the weather like today?",
-                        "Create a simple bar chart using Python",
-                        "Convert 100 USD to EUR",
-                        "What are the benefits of renewable energy?",
-                    ],
-                    inputs=question_input,
-                    label="Research & Analysis"
-                )
-            with gr.Column():
-                gr.Examples(
-                    examples=[
-                        "Analyze this image and describe what you see",
-                        "Extract text from this image using OCR",
-                        "Summarize the content of this document",
-                        "Analyze the data in this CSV file",
-                        "What insights can you find in this Excel file?",
-                    ],
-                    inputs=question_input,
-                    label="File Analysis"
-                )
-        
-        # Event handlers
-        def submit_question(question, history, files):
-            print(f"🎯 UI: Submit button clicked")
-            print(f"📝 UI: Question length: {len(question) if question else 0}")
-            print(f"📁 UI: Files count: {len(files) if files else 0}")
-            result_question, result_history = chatbot.process_question(question, history, files)
-            print(f"🔄 UI: Returning results and clearing files")
-            return result_question, result_history, None  # Clear files after processing
-        
-        def clear_conversation():
-            print("🧹 UI: Clear conversation button clicked")
-            return chatbot.clear_history()
-        
-        def clear_files():
-            print("🗑️  UI: Clear files button clicked")
-            return None
-        
-        # Connect the events
-        submit_btn.click(
-            fn=submit_question,
-            inputs=[question_input, chatbot_interface, file_upload],
-            outputs=[question_input, chatbot_interface, file_upload],
-            show_progress=True
-        )
-        
-        question_input.submit(
-            fn=submit_question,
-            inputs=[question_input, chatbot_interface, file_upload],
-            outputs=[question_input, chatbot_interface, file_upload],
-            show_progress=True
-        )
-        
-        clear_btn.click(
-            fn=clear_conversation,
-            outputs=[chatbot_interface],
-            show_progress=False
-        )
-        
-        clear_files_btn.click(
-            fn=clear_files,
-            outputs=[file_upload],
-            show_progress=False
-        )
-        
-        # Footer
-        gr.Markdown(
-            """
-            ---
-            
-            **Note:** This agent uses various tools and APIs to provide comprehensive answers. 
-            Processing complex questions and file analysis may take some time. Please be patient!
-            
-            **Supported file types:** 
-            - **Images:** JPG, PNG, GIF, BMP, WebP
-            - **Documents:** PDF, DOC, DOCX, TXT, MD
-            - **Data files:** CSV, Excel (XLS, XLSX)
-            - **Code files:** Python, JavaScript, HTML, CSS, JSON, XML
-            
-            **Powered by:** LangGraph, Groq, and various specialized tools
-            """
-        )
-    
-    return demo
-
-if __name__ == "__main__":
-    print("\n" + "-"*50)
-    print("🚀 Starting GAIA Agent Q&A Chatbot...")
-    print("-"*50 + "\n")
-    
-    # Create and launch the interface
-    demo = create_qna_interface()
-    demo.launch(
-        debug=True,
-        share=False,
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )