Update app.py

app.py

@@ -1,945 +1,207 @@
-import gradio as gr
-import time
+""" Basic Agent Evaluation Runner"""
 import os
-import base64
-from typing import List, Tuple, Optional
+import inspect
+import gradio as gr
+import requests
+import pandas as pd
 from langchain_core.messages import HumanMessage
 from agent import build_graph
 
-class QnAChatbot:
-    """A Q&A chatbot interface for the agent."""
-    
+
+
+# (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("🤖 QnAChatbot initializing...")
-        print("🔧 Building agent graph...")
+        print("BasicAgent initialized.")
         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
-        
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        messages = [HumanMessage(content=question)]
+        result = self.graph.invoke({"messages": messages})
+        answer = result['messages'][-1].content
+        return answer  # kein [14:] mehr nötig!
+
+
+
+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
         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
-            
+            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:
-            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 []
+             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}"})
 
-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...")
-    
-    # Enhanced Custom CSS for modern, professional styling
-    custom_css = """
-    /* Import Google Fonts */
-    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&family=JetBrains+Mono:wght@400;500&display=swap');
-    
-    /* CSS Variables for Theme Support */
-    :root {
-        --primary-gradient: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-        --secondary-gradient: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
-        --success-gradient: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);
-        --warning-gradient: linear-gradient(135deg, #43e97b 0%, #38f9d7 100%);
-        --glass-bg: rgba(255, 255, 255, 0.95);
-        --glass-border: rgba(255, 255, 255, 0.2);
-        --text-primary: #2d3748;
-        --text-secondary: #4a5568;
-        --text-light: #718096;
-        --bg-light: #f7fafc;
-        --bg-card: #ffffff;
-        --shadow-sm: 0 2px 10px rgba(0, 0, 0, 0.05);
-        --shadow-md: 0 10px 30px rgba(0, 0, 0, 0.1);
-        --shadow-lg: 0 20px 40px rgba(0, 0, 0, 0.15);
-        --border-radius: 12px;
-        --border-radius-lg: 20px;
-        --transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
-    }
-    
-    /* Global Styles */
-    * {
-        font-family: 'Inter', sans-serif !important;
-        box-sizing: border-box !important;
-    }
-    
-    /* Main Container with Enhanced Background */
-    .gradio-container {
-        max-width: 1400px !important;
-        margin: 0 auto !important;
-        background: 
-            radial-gradient(circle at 20% 50%, rgba(120, 119, 198, 0.3), transparent 50%),
-            radial-gradient(circle at 80% 20%, rgba(255, 119, 198, 0.3), transparent 50%),
-            radial-gradient(circle at 40% 80%, rgba(120, 219, 255, 0.3), transparent 50%),
-            var(--primary-gradient) !important;
-        min-height: 100vh !important;
-        padding: 20px !important;
-        position: relative !important;
-        overflow-x: hidden !important;
-    }
-    
-    /* Animated Background Particles */
-    .gradio-container::before {
-        content: '' !important;
-        position: fixed !important;
-        top: 0 !important;
-        left: 0 !important;
-        width: 100% !important;
-        height: 100% !important;
-        background: url("data:image/svg+xml,%3Csvg width='60' height='60' viewBox='0 0 60 60' xmlns='http://www.w3.org/2000/svg'%3E%3Cg fill='none' fill-rule='evenodd'%3E%3Cg fill='%23ffffff' fill-opacity='0.05'%3E%3Ccircle cx='7' cy='7' r='1'/%3E%3Ccircle cx='53' cy='7' r='1'/%3E%3Ccircle cx='7' cy='53' r='1'/%3E%3Ccircle cx='53' cy='53' r='1'/%3E%3C/g%3E%3C/g%3E%3C/svg%3E") !important;
-        animation: float 20s ease-in-out infinite !important;
-        pointer-events: none !important;
-        z-index: 0 !important;
-    }
-    
-    /* Main Content Area with Glass Effect */
-    .main-content {
-        background: var(--glass-bg) !important;
-        backdrop-filter: blur(20px) !important;
-        -webkit-backdrop-filter: blur(20px) !important;
-        border-radius: var(--border-radius-lg) !important;
-        box-shadow: 
-            var(--shadow-lg),
-            inset 0 1px 0 var(--glass-border) !important;
-        padding: 40px !important;
-        margin: 20px 0 !important;
-        position: relative !important;
-        z-index: 1 !important;
-        border: 1px solid var(--glass-border) !important;
-        transition: var(--transition) !important;
-    }
-    
-    .main-content:hover {
-        transform: translateY(-2px) !important;
-        box-shadow: 
-            0 25px 50px rgba(0, 0, 0, 0.15),
-            inset 0 1px 0 var(--glass-border) !important;
-    }
-    
-    /* Enhanced Header with Animations */
-    .markdown h1 {
-        background: var(--primary-gradient) !important;
-        -webkit-background-clip: text !important;
-        -webkit-text-fill-color: transparent !important;
-        background-clip: text !important;
-        font-size: 3rem !important;
-        font-weight: 800 !important;
-        text-align: center !important;
-        margin-bottom: 1.5rem !important;
-        position: relative !important;
-        animation: titleGlow 3s ease-in-out infinite alternate !important;
-    }
-    
-    .markdown h1::after {
-        content: '' !important;
-        position: absolute !important;
-        bottom: -10px !important;
-        left: 50% !important;
-        transform: translateX(-50%) !important;
-        width: 100px !important;
-        height: 4px !important;
-        background: var(--primary-gradient) !important;
-        border-radius: 2px !important;
-        animation: pulse 2s ease-in-out infinite !important;
-    }
-    
-    /* Enhanced Chat Interface */
-    .chatbot {
-        border: none !important;
-        border-radius: var(--border-radius) !important;
-        box-shadow: var(--shadow-md) !important;
-        background: var(--bg-card) !important;
-        overflow: hidden !important;
-        position: relative !important;
-    }
-    
-    /* Chat Messages with Better Styling */
-    .chatbot .message-wrap {
-        padding: 20px !important;
-        margin: 15px !important;
-        border-radius: 18px !important;
-        max-width: 85% !important;
-        animation: messageSlideIn 0.5s cubic-bezier(0.4, 0, 0.2, 1) !important;
-        position: relative !important;
-        word-wrap: break-word !important;
-    }
-    
-    .chatbot .message.user {
-        background: var(--primary-gradient) !important;
-        color: white !important;
-        margin-left: auto !important;
-        margin-right: 15px !important;
-        box-shadow: 0 8px 25px rgba(102, 126, 234, 0.4) !important;
-        border-bottom-right-radius: 5px !important;
-    }
-    
-    .chatbot .message.user::before {
-        content: '🧑‍💻' !important;
-        position: absolute !important;
-        top: -25px !important;
-        right: 10px !important;
-        font-size: 16px !important;
-        background: var(--bg-card) !important;
-        padding: 5px 8px !important;
-        border-radius: 20px !important;
-        box-shadow: var(--shadow-sm) !important;
-    }
-    
-    .chatbot .message.bot {
-        background: linear-gradient(135deg, #f8f9ff 0%, #e8eeff 100%) !important;
-        color: var(--text-primary) !important;
-        margin-right: auto !important;
-        margin-left: 15px !important;
-        border: 1px solid #e2e8f0 !important;
-        box-shadow: var(--shadow-sm) !important;
-        border-bottom-left-radius: 5px !important;
-    }
-    
-    .chatbot .message.bot::before {
-        content: '🤖' !important;
-        position: absolute !important;
-        top: -25px !important;
-        left: 10px !important;
-        font-size: 16px !important;
-        background: var(--bg-card) !important;
-        padding: 5px 8px !important;
-        border-radius: 20px !important;
-        box-shadow: var(--shadow-sm) !important;
-    }
-    
-    /* Typing Indicator */
-    .typing-indicator {
-        display: flex !important;
-        align-items: center !important;
-        padding: 15px 20px !important;
-        margin: 15px !important;
-        background: linear-gradient(135deg, #f8f9ff 0%, #e8eeff 100%) !important;
-        border-radius: 18px !important;
-        max-width: 85% !important;
-        margin-right: auto !important;
-        margin-left: 15px !important;
-        border: 1px solid #e2e8f0 !important;
-        animation: messageSlideIn 0.5s cubic-bezier(0.4, 0, 0.2, 1) !important;
-    }
-    
-    .typing-dots {
-        display: flex !important;
-        align-items: center !important;
-        gap: 4px !important;
-    }
-    
-    .typing-dots span {
-        width: 8px !important;
-        height: 8px !important;
-        border-radius: 50% !important;
-        background: var(--text-light) !important;
-        animation: typingDots 1.4s ease-in-out infinite both !important;
-    }
-    
-    .typing-dots span:nth-child(1) { animation-delay: -0.32s !important; }
-    .typing-dots span:nth-child(2) { animation-delay: -0.16s !important; }
-    .typing-dots span:nth-child(3) { animation-delay: 0s !important; }
-    
-    /* Enhanced Input Areas */
-    .textbox input, .textbox textarea {
-        border: 2px solid #e2e8f0 !important;
-        border-radius: var(--border-radius) !important;
-        padding: 18px 24px !important;
-        font-size: 16px !important;
-        line-height: 1.5 !important;
-        transition: var(--transition) !important;
-        background: var(--bg-card) !important;
-        box-shadow: var(--shadow-sm) !important;
-        font-family: 'Inter', sans-serif !important;
-    }
-    
-    .textbox input:focus, .textbox textarea:focus {
-        border-color: #667eea !important;
-        box-shadow: 
-            0 0 0 4px rgba(102, 126, 234, 0.1),
-            var(--shadow-md) !important;
-        outline: none !important;
-        transform: translateY(-1px) !important;
-        background: #ffffff !important;
-    }
-    
-    /* Enhanced Buttons with Micro-interactions */
-    .btn {
-        border-radius: var(--border-radius) !important;
-        font-weight: 600 !important;
-        text-transform: none !important;
-        letter-spacing: 0.3px !important;
-        transition: var(--transition) !important;
-        border: none !important;
-        box-shadow: var(--shadow-sm) !important;
-        padding: 14px 28px !important;
-        font-size: 16px !important;
-        position: relative !important;
-        overflow: hidden !important;
-        cursor: pointer !important;
-    }
-    
-    .btn::before {
-        content: '' !important;
-        position: absolute !important;
-        top: 0 !important;
-        left: -100% !important;
-        width: 100% !important;
-        height: 100% !important;
-        background: linear-gradient(90deg, transparent, rgba(255,255,255,0.3), transparent) !important;
-        transition: left 0.5s !important;
-    }
-    
-    .btn:hover::before {
-        left: 100% !important;
-    }
-    
-    .btn-primary {
-        background: var(--primary-gradient) !important;
-        color: white !important;
-        box-shadow: 0 8px 25px rgba(102, 126, 234, 0.3) !important;
-    }
-    
-    .btn-primary:hover {
-        transform: translateY(-3px) scale(1.02) !important;
-        box-shadow: 0 12px 35px rgba(102, 126, 234, 0.4) !important;
-    }
-    
-    .btn-primary:active {
-        transform: translateY(-1px) scale(0.98) !important;
-    }
-    
-    .btn-secondary {
-        background: linear-gradient(135deg, #f7fafc 0%, #edf2f7 100%) !important;
-        color: var(--text-secondary) !important;
-        border: 1px solid #e2e8f0 !important;
-    }
-    
-    .btn-secondary:hover {
-        transform: translateY(-2px) !important;
-        background: linear-gradient(135deg, #edf2f7 0%, #e2e8f0 100%) !important;
-        box-shadow: var(--shadow-md) !important;
-    }
-    
-    /* Enhanced File Upload with Drag & Drop Animation */
-    .file-upload {
-        border: 3px dashed #cbd5e0 !important;
-        border-radius: var(--border-radius) !important;
-        background: linear-gradient(135deg, #f7fafc 0%, #edf2f7 100%) !important;
-        padding: 40px !important;
-        text-align: center !important;
-        transition: var(--transition) !important;
-        position: relative !important;
-        overflow: hidden !important;
-        cursor: pointer !important;
-    }
-    
-    .file-upload:hover {
-        border-color: #667eea !important;
-        background: linear-gradient(135deg, #edf2f7 0%, #e2e8f0 100%) !important;
-        transform: translateY(-2px) scale(1.01) !important;
-        box-shadow: var(--shadow-md) !important;
-    }
-    
-    .file-upload::before {
-        content: '📁' !important;
-        font-size: 4rem !important;
-        display: block !important;
-        margin-bottom: 15px !important;
-        animation: fileFloat 3s ease-in-out infinite !important;
-    }
-    
-    .file-upload::after {
-        content: 'Drag files here or click to browse' !important;
-        position: absolute !important;
-        bottom: 15px !important;
-        left: 50% !important;
-        transform: translateX(-50%) !important;
-        font-size: 14px !important;
-        color: var(--text-light) !important;
-        font-weight: 500 !important;
-    }
-    
-    /* Enhanced Examples Section */
-    .examples {
-        background: linear-gradient(135deg, #f8f9ff 0%, #e8eeff 100%) !important;
-        border-radius: var(--border-radius) !important;
-        padding: 30px !important;
-        margin: 25px 0 !important;
-        border: 1px solid #e2e8f0 !important;
-        box-shadow: var(--shadow-sm) !important;
-        position: relative !important;
-    }
-    
-    .examples::before {
-        content: '💡' !important;
-        position: absolute !important;
-        top: -15px !important;
-        left: 30px !important;
-        background: var(--bg-card) !important;
-        padding: 10px !important;
-        border-radius: 50% !important;
-        font-size: 20px !important;
-        box-shadow: var(--shadow-sm) !important;
-    }
-    
-    .examples h3 {
-        color: #667eea !important;
-        font-weight: 700 !important;
-        margin-bottom: 20px !important;
-        font-size: 1.3rem !important;
-        margin-left: 20px !important;
-    }
-    
-    /* Feature Cards with Hover Effects */
-    .feature-card {
-        background: var(--bg-card) !important;
-        border-radius: var(--border-radius) !important;
-        padding: 25px !important;
-        margin: 15px 0 !important;
-        box-shadow: var(--shadow-sm) !important;
-        border-left: 4px solid transparent !important;
-        transition: var(--transition) !important;
-        cursor: pointer !important;
-    }
-    
-    .feature-card:hover {
-        transform: translateY(-5px) translateX(5px) !important;
-        box-shadow: var(--shadow-md) !important;
-    }
-    
-    .feature-card.research {
-        border-left-color: #667eea !important;
-    }
-    
-    .feature-card.code {
-        border-left-color: #48bb78 !important;
-    }
-    
-    .feature-card.data {
-        border-left-color: #ed8936 !important;
-    }
-    
-    .feature-card.image {
-        border-left-color: #dd6b20 !important;
-    }
-    
-    /* Status Indicator with Pulse Animation */
-    .status-indicator {
-        display: inline-block !important;
-        width: 12px !important;
-        height: 12px !important;
-        border-radius: 50% !important;
-        background: radial-gradient(circle, #48bb78, #38a169) !important;
-        margin-right: 12px !important;
-        animation: statusPulse 2s ease-in-out infinite !important;
-        box-shadow: 0 0 0 0 rgba(72, 187, 120, 0.7) !important;
-    }
-    
-    /* Enhanced Footer */
-    .footer {
-        background: var(--glass-bg) !important;
-        backdrop-filter: blur(10px) !important;
-        border-radius: var(--border-radius) !important;
-        padding: 30px !important;
-        margin-top: 40px !important;
-        text-align: center !important;
-        border: 1px solid var(--glass-border) !important;
-        box-shadow: var(--shadow-sm) !important;
-    }
-    
-    /* Advanced Animations */
-    @keyframes titleGlow {
-        0%, 100% { text-shadow: 0 0 20px rgba(102, 126, 234, 0.5); }
-        50% { text-shadow: 0 0 30px rgba(102, 126, 234, 0.8), 0 0 40px rgba(118, 75, 162, 0.6); }
-    }
-    
-    @keyframes messageSlideIn {
-        from {
-            opacity: 0;
-            transform: translateY(30px) scale(0.95);
-        }
-        to {
-            opacity: 1;
-            transform: translateY(0) scale(1);
-        }
-    }
-    
-    @keyframes typingDots {
-        0%, 80%, 100% { transform: scale(0.8); opacity: 0.5; }
-        40% { transform: scale(1.2); opacity: 1; }
-    }
-    
-    @keyframes fileFloat {
-        0%, 100% { transform: translateY(0px); }
-        50% { transform: translateY(-10px); }
-    }
-    
-    @keyframes statusPulse {
-        0% {
-            transform: scale(0.95);
-            box-shadow: 0 0 0 0 rgba(72, 187, 120, 0.7);
-        }
-        70% {
-            transform: scale(1);
-            box-shadow: 0 0 0 10px rgba(72, 187, 120, 0);
-        }
-        100% {
-            transform: scale(0.95);
-            box-shadow: 0 0 0 0 rgba(72, 187, 120, 0);
-        }
-    }
-    
-    @keyframes float {
-        0%, 100% { transform: translateY(0px) rotate(0deg); }
-        33% { transform: translateY(-10px) rotate(1deg); }
-        66% { transform: translateY(-5px) rotate(-1deg); }
-    }
-    
-    @keyframes pulse {
-        0%, 100% { transform: scale(1) scaleX(1); }
-        50% { transform: scale(1.05) scaleX(1.1); }
-    }
-    
-    /* Responsive Design Enhancements */
-    @media (max-width: 1024px) {
-        .gradio-container { padding: 15px !important; }
-        .main-content { padding: 25px !important; margin: 15px 0 !important; }
-        .markdown h1 { font-size: 2.5rem !important; }
-    }
-    
-    @media (max-width: 768px) {
-        .gradio-container { padding: 10px !important; }
-        .main-content { padding: 20px !important; margin: 10px 0 !important; }
-        .chatbot .message-wrap { max-width: 90% !important; margin: 10px !important; padding: 15px !important; }
-        .markdown h1 { font-size: 2rem !important; }
-        .btn { padding: 12px 20px !important; font-size: 14px !important; }
-        .file-upload { padding: 30px 20px !important; }
-    }
-    
-    @media (max-width: 480px) {
-        .markdown h1 { font-size: 1.8rem !important; }
-        .chatbot .message-wrap { margin: 8px !important; padding: 12px !important; }
-        .main-content { padding: 15px !important; }
-    }
-    
-    /* Custom Scrollbar Enhancement */
-    ::-webkit-scrollbar { width: 12px; }
-    ::-webkit-scrollbar-track { 
-        background: rgba(241, 241, 241, 0.5); 
-        border-radius: 6px; 
-    }
-    ::-webkit-scrollbar-thumb { 
-        background: var(--primary-gradient); 
-        border-radius: 6px; 
-        border: 2px solid rgba(255, 255, 255, 0.2);
-    }
-    ::-webkit-scrollbar-thumb:hover { 
-        background: linear-gradient(135deg, #5a6fd8 0%, #6a4190 100%); 
-    }
-    
-    /* Loading States */
-    .loading { animation: pulse 2s infinite !important; }
-    .processing { 
-        position: relative !important;
-        overflow: hidden !important;
-    }
-    .processing::after {
-        content: '' !important;
-        position: absolute !important;
-        top: 0 !important;
-        left: -100% !important;
-        width: 100% !important;
-        height: 100% !important;
-        background: linear-gradient(90deg, transparent, rgba(255,255,255,0.4), transparent) !important;
-        animation: shimmer 2s infinite !important;
-    }
-    
-    @keyframes shimmer {
-        0% { left: -100%; }
-        100% { left: 100%; }
-    }
-    
-    /* Dark mode support (future enhancement) */
-    @media (prefers-color-scheme: dark) {
-        :root {
-            --glass-bg: rgba(26, 32, 44, 0.95);
-            --text-primary: #e2e8f0;
-            --text-secondary: #cbd5e0;
-            --text-light: #a0aec0;
-            --bg-card: #2d3748;
-            --bg-light: #1a202c;
-        }
-    }
-    """
-    
-    with gr.Blocks(css=custom_css, title="GAIA Agent - Q&A Chatbot", theme=gr.themes.Soft()) as demo:
-        # Header with enhanced styling
-        with gr.Row(elem_classes="main-content"):
-            gr.Markdown(
-                """
-                <h1 align="center">🤖 GAIA Agent - Advanced Q&A Chatbot</h1>
-                
-                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:**
-                
-                <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 20px; margin: 20px 0;">
-                    <div class="feature-card research" style="background: linear-gradient(135deg, #f8f9ff 0%, #e8eeff 100%); padding: 25px; border-radius: 12px; border-left: 4px solid #667eea; cursor: pointer; transition: all 0.3s ease;">
-                        <div style="display: flex; align-items: center; margin-bottom: 10px;">
-                            <span style="font-size: 2rem; margin-right: 15px;">🔍</span>
-                            <strong style="font-size: 1.1rem; color: #667eea;">Research & Search</strong>
-                        </div>
-                        <p style="margin: 0; color: #718096; font-size: 14px; line-height: 1.5;">Web search, Wikipedia, academic papers, arXiv research</p>
-                    </div>
-                    <div class="feature-card code" style="background: linear-gradient(135deg, #f0fff4 0%, #c6f6d5 100%); padding: 25px; border-radius: 12px; border-left: 4px solid #48bb78; cursor: pointer; transition: all 0.3s ease;">
-                        <div style="display: flex; align-items: center; margin-bottom: 10px;">
-                            <span style="font-size: 2rem; margin-right: 15px;">💻</span>
-                            <strong style="font-size: 1.1rem; color: #48bb78;">Code Execution</strong>
-                        </div>
-                        <p style="margin: 0; color: #718096; font-size: 14px; line-height: 1.5;">Python, Bash, SQL, C, Java with real-time results</p>
-                    </div>
-                    <div class="feature-card data" style="background: linear-gradient(135deg, #fffaf0 0%, #fbd38d 100%); padding: 25px; border-radius: 12px; border-left: 4px solid #ed8936; cursor: pointer; transition: all 0.3s ease;">
-                        <div style="display: flex; align-items: center; margin-bottom: 10px;">
-                            <span style="font-size: 2rem; margin-right: 15px;">📊</span>
-                            <strong style="font-size: 1.1rem; color: #ed8936;">Data Analysis</strong>
-                        </div>
-                        <p style="margin: 0; color: #718096; font-size: 14px; line-height: 1.5;">CSV, Excel, visualizations, statistical analysis</p>
-                    </div>
-                    <div class="feature-card image" style="background: linear-gradient(135deg, #fef5e7 0%, #f6ad55 100%); padding: 25px; border-radius: 12px; border-left: 4px solid #dd6b20; cursor: pointer; transition: all 0.3s ease;">
-                        <div style="display: flex; align-items: center; margin-bottom: 10px;">
-                            <span style="font-size: 2rem; margin-right: 15px;">🖼️</span>
-                            <strong style="font-size: 1.1rem; color: #dd6b20;">Image Processing</strong>
-                        </div>
-                        <p style="margin: 0; color: #718096; font-size: 14px; line-height: 1.5;">Analysis, OCR, transformations, generation</p>
-                    </div>
-                </div>
-                
-                ---
-                """
-            )
-        
-        # Chat interface with enhanced styling
-        with gr.Row(elem_classes="main-content"):
-            with gr.Column(scale=1):
-                chatbot_interface = gr.Chatbot(
-                    label="💬 Conversation",
-                    height=600,
-                    show_label=True,
-                    container=True,
-                    bubble_full_width=False,
-                    elem_classes="chatbot"
-                )
-        
-        # File upload section with enhanced styling
-        with gr.Row(elem_classes="main-content"):
-            with gr.Column():
-                file_upload = gr.File(
-                    label="📁 Upload Files - Drag & drop or click to upload images, documents, CSV, Excel files, 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=120,
-                    elem_classes="file-upload"
-                )
-        
-        # Input and buttons with enhanced styling
-        with gr.Row(elem_classes="main-content"):
-            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=3,
-                    max_lines=5,
-                    elem_classes="textbox"
-                )
-            with gr.Column(scale=2, min_width=120):
-                submit_btn = gr.Button("🚀 Send", variant="primary", size="lg", elem_classes="btn btn-primary")
-        
-        with gr.Row(elem_classes="main-content"):
-            with gr.Column(scale=1):
-                clear_btn = gr.Button("🧹 Clear History", variant="secondary", elem_classes="btn btn-secondary")
-            with gr.Column(scale=1):
-                clear_files_btn = gr.Button("🗑️ Clear Files", variant="secondary", elem_classes="btn btn-secondary")
-            with gr.Column(scale=1):
-                export_btn = gr.Button("💾 Export Chat", variant="secondary", elem_classes="btn btn-secondary")
-                
-        # Hidden download component for chat export
-        download_file = gr.File(visible=False)
-            
-        with gr.Row(elem_classes="main-content"):
-            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
-            
-        def export_conversation(history):
-            """Export conversation history to a text file"""
-            print("💾 UI: Export conversation button clicked")
-            if not history:
-                print("⚠️  No conversation to export")
-                return None
-                
-            try:
-                import tempfile
-                import datetime
-                
-                # Create export content
-                timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-                export_content = f"# GAIA Agent Conversation Export\n"
-                export_content += f"Export Date: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n"
-                export_content += f"Total Messages: {len(history)}\n\n"
-                export_content += "=" * 50 + "\n\n"
-                
-                for i, (user_msg, bot_msg) in enumerate(history, 1):
-                    export_content += f"## Message {i}\n\n"
-                    export_content += f"**User:** {user_msg}\n\n"
-                    export_content += f"**Assistant:** {bot_msg}\n\n"
-                    export_content += "-" * 30 + "\n\n"
-                
-                # Save to temporary file
-                temp_file = tempfile.NamedTemporaryFile(
-                    mode='w', 
-                    suffix=f'_gaia_chat_export_{timestamp}.md',
-                    delete=False,
-                    encoding='utf-8'
-                )
-                temp_file.write(export_content)
-                temp_file.close()
-                
-                print(f"📄 Conversation exported to: {temp_file.name}")
-                return temp_file.name
-                
-            except Exception as e:
-                print(f"❌ Error exporting conversation: {e}")
-                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
-        )
-        
-        export_btn.click(
-            fn=export_conversation,
-            inputs=[chatbot_interface],
-            outputs=[download_file],
-            show_progress=True
+    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.')}"
         )
-    
-    return demo
+        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" + "-"*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
-    ) 
+    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)