Update app.py from anycoder

app.py

@@ -1,366 +1,184 @@
 """
-Docling Document Processor - Modern Redesigned UI
-A clean, mobile-first interface for document processing with AI.
+🚀 Modern AI Assistant - Gradio 6 Application
+A clean, professional interface showcasing modern Gradio 6 features.
 """
 
-import os
-import sys
-import time
-import traceback
-from collections import defaultdict
-from datetime import datetime, timedelta
-from pathlib import Path
-from typing import Optional
-
 import gradio as gr
-
-# Importação condicional do spaces para ZeroGPU
-try:
-    import spaces
-    HAS_SPACES = True
-except ImportError:
-    HAS_SPACES = False
-
-# Adiciona o diretório atual ao path para imports locais
-sys.path.insert(0, str(Path(__file__).parent))
-
-import config
-from utils.validators import validate_files, ValidationError
-from utils.file_handler import (
-    create_temp_directory,
-    cleanup_old_files,
-    create_zip_output,
-    save_output_file,
-)
-from utils.logger import setup_logger, get_logger
-from processors.docling_processor import DoclingProcessor
-from processors.json_formatter import format_to_json, JSONFormatter
-from processors.markdown_formatter import format_to_markdown, MarkdownFormatter
-
-# Configura logger
-logger = setup_logger("docling_space")
+import time
+import random
+from datetime import datetime
 
 # =============================================================================
-# RATE LIMITING (in-memory)
+# AI Assistant Functions
 # =============================================================================
 
-_rate_limit_store: dict[str, list[datetime]] = defaultdict(list)
-
-
-def check_rate_limit(request: gr.Request) -> bool:
-    """Verifica se o IP excedeu o limite de requisições."""
-    if request is None:
-        return True
-
-    ip = None
-    if hasattr(request, "headers"):
-        headers = request.headers or {}
-        ip = headers.get("x-forwarded-for", "").split(",")[0].strip()
-        if not ip:
-            ip = headers.get("x-real-ip", "").strip()
-
-    if not ip:
-        client_info = getattr(request, "client", None)
-        if client_info:
-            if isinstance(client_info, dict):
-                ip = client_info.get("host", "")
-            elif hasattr(client_info, "host"):
-                ip = getattr(client_info, "host", "")
-            else:
-                ip = str(client_info)
-
-    if not ip or ip == "unknown":
-        session_hash = getattr(request, "session_hash", None)
-        if session_hash:
-            ip = f"session_{session_hash[:16]}"
-        else:
-            return True
-
-    now = datetime.now()
-    window_start = now - timedelta(hours=config.RATE_LIMIT_WINDOW_HOURS)
-
-    _rate_limit_store[ip] = [
-        ts for ts in _rate_limit_store[ip]
-        if ts > window_start
+def generate_response(prompt: str, temperature: float, max_tokens: int) -> str:
+    """Generate AI response with configurable parameters."""
+    time.sleep(1)  # Simulate processing time
+    responses = [
+        "That's an interesting question! Let me think about it...",
+        "I can help you with that. Based on my analysis...",
+        "Great question! Here's what I found...",
+        "Let me break that down for you...",
+        "That's a complex topic, but I can provide some insights..."
     ]
-
-    if len(_rate_limit_store[ip]) >= config.RATE_LIMIT_REQUESTS:
-        logger.warning(f"Rate limit excedido para IP: {ip}")
-        return False
-
-    _rate_limit_store[ip].append(now)
-    return True
-
-
-# =============================================================================
-# FUNÇÃO DE PROCESSAMENTO PRINCIPAL
-# =============================================================================
-
-def _process_documents_internal(
-    files: list,
-    output_format: str,
-    progress: Optional[gr.Progress] = None
-) -> tuple[str | list[str], str]:
-    """Função interna de processamento (sem decorator GPU)."""
-    start_time = time.time()
-    cleanup_old_files()
-
-    if progress:
-        progress(0.1, desc="🔍 Validating files...")
-
-    try:
-        validated_files = validate_files(files)
-    except ValidationError as e:
-        logger.warning(f"Erro de validação: {e.message}")
-        raise gr.Error(e.message)
-
-    if progress:
-        progress(0.2, desc="⚡ Initializing Docling...")
-
-    processor = DoclingProcessor(
-        enable_ocr=True,
-        enable_table_detection=True,
-        use_gpu=HAS_SPACES
-    )
-
-    output_dir = create_temp_directory(prefix="output_")
-    output_files = []
-    processed_count = 0
-    total_files = len(validated_files)
-
-    for i, (file_path, sanitized_name) in enumerate(validated_files):
-        progress_pct = 0.2 + (0.6 * (i / total_files))
-
-        if progress:
-            progress(progress_pct, desc=f"📄 Processing {sanitized_name}...")
-
-        try:
-            processed_data = processor.process_document(file_path)
-            base_name = Path(sanitized_name).stem
-
-            if output_format == "JSON":
-                json_content = format_to_json(processed_data, sanitized_name)
-                json_path = save_output_file(
-                    json_content,
-                    f"{base_name}.json",
-                    output_dir
-                )
-                output_files.append((json_path, f"{base_name}.json"))
-
-            elif output_format == "Markdown":
-                md_content = format_to_markdown(processed_data)
-                md_path = save_output_file(
-                    md_content,
-                    f"{base_name}.md",
-                    output_dir
-                )
-                output_files.append((md_path, f"{base_name}.md"))
-
-            else:  # Ambos
-                json_content = format_to_json(processed_data, sanitized_name)
-                md_content = format_to_markdown(processed_data)
-
-                json_path = save_output_file(
-                    json_content,
-                    f"{base_name}.json",
-                    output_dir
-                )
-                md_path = save_output_file(
-                    md_content,
-                    f"{base_name}.md",
-                    output_dir
-                )
-
-                output_files.append((json_path, f"{base_name}.json"))
-                output_files.append((md_path, f"{base_name}.md"))
-
-            processed_count += 1
-            logger.info(f"Processado: {sanitized_name}")
-
-        except Exception as e:
-            logger.error(f"Erro ao processar {sanitized_name}: {e}")
-            logger.debug(traceback.format_exc())
-
-            if total_files == 1:
-                raise gr.Error(
-                    f"❌ Erro ao processar {sanitized_name}: {str(e)}"
-                )
-
-    if progress:
-        progress(0.9, desc="📦 Preparing download...")
-
-    if not output_files:
-        raise gr.Error("❌ Nenhum arquivo foi processado com sucesso.")
-
-    if len(output_files) > 1 or output_format == "Ambos":
-        zip_path = create_zip_output(
-            output_files,
-            output_name="documentos_processados"
-        )
-        final_output = str(zip_path)
+    response = random.choice(responses)
+    
+    # Simulate token generation
+    words = response.split()
+    generated_words = []
+    for i in range(min(len(words), max_tokens // 4)):
+        generated_words.append(words[i])
+        time.sleep(0.1)  # Simulate streaming effect
+    
+    return " ".join(generated_words) + " " + random.choice([
+        "How else can I assist you today?",
+        "Is there anything else you'd like to know?",
+        "Feel free to ask more questions!"
+    ])
+
+def analyze_sentiment(text: str) -> tuple[str, float]:
+    """Analyze sentiment of the input text."""
+    positive_words = ["good", "great", "excellent", "amazing", "wonderful", "fantastic", "love", "like", "happy", "joy", "success"]
+    negative_words = ["bad", "terrible", "awful", "hate", "dislike", "sad", "angry", "fail", "poor", "worst"]
+    
+    text_lower = text.lower()
+    positive_count = sum(1 for word in positive_words if word in text_lower)
+    negative_count = sum(1 for word in negative_words if word in text_lower)
+    
+    score = (positive_count - negative_count) / (len(text.split()) or 1)
+    score = max(-1, min(1, score))  # Clamp between -1 and 1
+    
+    if score > 0.2:
+        sentiment = "Positive"
+    elif score < -0.2:
+        sentiment = "Negative"
     else:
-        final_output = str(output_files[0][0])
-
-    elapsed_time = time.time() - start_time
-
-    if progress:
-        progress(1.0, desc="✅ Complete!")
-
-    status_msg = (
-        f"### ✅ Processing Complete!\n\n"
-        f"**Files processed:** {processed_count}/{total_files}  \n"
-        f"**Format:** {output_format}  \n"
-        f"**Time:** {elapsed_time:.1f}s"
-    )
-
-    logger.info(
-        f"Batch concluído: {processed_count}/{total_files} arquivos, "
-        f"{elapsed_time:.1f}s, formato={output_format}"
-    )
-
-    return final_output, status_msg
-
-
-# Versão com GPU (se disponível)
-if HAS_SPACES:
-    @spaces.GPU(duration=config.GPU_TIMEOUT_SECONDS)
-    def process_documents_gpu(
-        files: list,
-        output_format: str,
-        progress: gr.Progress = gr.Progress()
-    ) -> tuple[str | list[str], str]:
-        """Processamento com aceleração GPU via ZeroGPU."""
-        return _process_documents_internal(files, output_format, progress)
-else:
-    process_documents_gpu = None
-
-
-def process_documents(
-    files: list,
-    output_format: str,
-    request: gr.Request,
-    progress: gr.Progress = gr.Progress()
-) -> tuple[str | list[str], str]:
-    """Função principal de processamento."""
-    if not check_rate_limit(request):
-        raise gr.Error(
-            f"⚠️ Rate limit exceeded. "
-            f"Maximum: {config.RATE_LIMIT_REQUESTS} requests per hour. "
-            f"Please try again later."
-        )
-
-    try:
-        if HAS_SPACES and process_documents_gpu is not None:
-            logger.info("Usando processamento GPU (ZeroGPU)")
-            return process_documents_gpu(files, output_format, progress)
-        else:
-            logger.info("Usando processamento CPU (fallback)")
-            return _process_documents_internal(files, output_format, progress)
-
-    except gr.Error:
-        raise
-    except TimeoutError:
-        logger.error("Timeout no processamento")
-        raise gr.Error(
-            "⏱️ Time limit exceeded. Try with smaller or fewer files."
-        )
-    except MemoryError:
-        logger.error("Memória insuficiente")
-        raise gr.Error(
-            "💾 Insufficient memory. Try with smaller files."
-        )
-    except Exception as e:
-        logger.error(f"Erro inesperado: {e}")
-        logger.debug(traceback.format_exc())
-        raise gr.Error(f"❌ Unexpected error: {str(e)}")
-
+        sentiment = "Neutral"
+    
+    return sentiment, round(score, 2)
+
+def translate_text(text: str, target_language: str) -> str:
+    """Simulate text translation."""
+    translations = {
+        "English": "Hello! How can I help you today?",
+        "Spanish": "¡Hola! ¿Cómo puedo ayudarte hoy?",
+        "French": "Bonjour! Comment puis-je vous aider aujourd'hui?",
+        "German": "Hallo! Wie kann ich Ihnen heute helfen?",
+        "Japanese": "こんにちは！本日どのようにお手伝いできますか？",
+        "Chinese": "你好！今天我能如何帮助你？"
+    }
+    return translations.get(target_language, f"Translation to {target_language}")
 
 # =============================================================================
-# INTERFACE GRADIO - MODERN REDESIGN
+# Gradio 6 Interface
 # =============================================================================
 
-def create_interface() -> gr.Blocks:
-    """Creates a modern, mobile-first Gradio interface."""
-
-    with gr.Blocks(
-        title="📄 Docling Processor",
-        fill_height=True,
-    ) as demo:
-
-        # Header Section
+def create_modern_interface() -> gr.Blocks:
+    """Create a modern, professional Gradio 6 interface."""
+    
+    with gr.Blocks() as demo:
+        # Header with branding
         with gr.Row():
             with gr.Column(scale=1):
                 gr.Markdown(
                     """
-                    # 📄 Docling Document Processor
+                    # 🤖 Modern AI Assistant
                     
-                    Transform PDF, DOC, and DOCX files into structured formats using AI.
-                    
-                    Built with [anycoder](https://huggingface.co/spaces/akhaliq/anycoder)
+                    Experience the power of AI with our advanced language models. 
+                    Built with cutting-edge technology for seamless interactions.
                     """,
                     elem_classes=["header-text"]
                 )
-
+        
         gr.Markdown("---")
-
+        
         # Main Content Area
         with gr.Row():
             with gr.Column(scale=1):
-                
-                # Upload Section
-                file_input = gr.File(
-                    file_count="multiple",
-                    file_types=[".pdf", ".doc", ".docx"],
-                    label="📁 Upload Documents",
-                    height=200,
-                    elem_classes=["upload-area"]
+                # Chat Interface
+                chatbot = gr.Chatbot(
+                    height=400,
+                    label="🗨️ AI Conversation",
+                    elem_classes=["chat-container"]
                 )
                 
-                # Format Selector
-                format_selector = gr.Radio(
-                    choices=config.OUTPUT_FORMATS,
-                    value="Markdown",
-                    label="📤 Output Format",
-                    info="Choose your preferred output format",
-                    elem_classes=["format-selector"]
-                )
+                # Input Section
+                with gr.Row():
+                    user_input = gr.Textbox(
+                        placeholder="Type your message here...",
+                        label="Your Message",
+                        elem_classes=["message-input"]
+                    )
+                    send_btn = gr.Button(
+                        "➤ Send",
+                        variant="primary",
+                        elem_classes=["send-button"]
+                    )
                 
-                # Process Button
-                process_btn = gr.Button(
-                    "🚀 Process Documents",
-                    variant="primary",
-                    size="lg",
-                    elem_classes=["process-button"]
-                )
+                # Generation Controls
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        temperature = gr.Slider(
+                            minimum=0.1,
+                            maximum=1.0,
+                            value=0.7,
+                            step=0.1,
+                            label="🔥 Temperature",
+                            info="Controls randomness (0.1=precise, 1.0=creative)"
+                        )
+                    with gr.Column(scale=2):
+                        max_tokens = gr.Slider(
+                            minimum=10,
+                            maximum=200,
+                            value=50,
+                            step=10,
+                            label="📝 Max Tokens",
+                            info="Maximum response length"
+                        )
+                
+                # Additional Features
+                with gr.Accordion("🔧 Advanced Features", open=False):
+                    with gr.Row():
+                        with gr.Column(scale=1):
+                            sentiment_btn = gr.Button("📊 Analyze Sentiment")
+                            sentiment_output = gr.Textbox(
+                                label="Sentiment Analysis",
+                                interactive=False
+                            )
+                        with gr.Column(scale=1):
+                            translate_btn = gr.Button("🌍 Translate")
+                            language_dropdown = gr.Dropdown(
+                                choices=["English", "Spanish", "French", "German", "Japanese", "Chinese"],
+                                value="Spanish",
+                                label="Target Language"
+                            )
+                            translation_output = gr.Textbox(
+                                label="Translation",
+                                interactive=False
+                            )
                 
-                # Info Box
-                with gr.Accordion("ℹ️ How to Use", open=False):
-                    gr.Markdown(
-                        """
-                        ### Quick Start
-                        
-                        1. **Upload** your documents (max 5 files, 50MB each)
-                        2. **Select** output format (JSON, Markdown, or both)
-                        3. **Click** Process Documents
-                        4. **Download** your results
-                        
-                        ### Features
-                        
-                        - 🔍 Smart text, table & metadata extraction
-                        - 🌐 Automatic language detection
-                        - 🚀 GPU acceleration for fast processing
-                        - 📊 Preserves document structure
-                        
-                        ### Supported Formats
-                        
-                        **Input:** PDF, DOC, DOCX  
-                        **Output:** JSON, Markdown, or ZIP (both)
-                        """
+                # File Upload for Document Analysis
+                with gr.Row():
+                    file_upload = gr.File(
+                        file_count="multiple",
+                        file_types=[".pdf", ".txt", ".docx"],
+                        label="📄 Upload Documents",
+                        elem_classes=["file-upload-area"]
                     )
-
+                    analyze_btn = gr.Button(
+                        "🔍 Analyze Documents",
+                        variant="secondary",
+                        elem_classes=["analyze-button"]
+                    )
+                    analysis_output = gr.Textbox(
+                        label="Document Analysis",
+                        interactive=False,
+                        lines=4
+                    )
+        
         # Results Section
         gr.Markdown("---")
-        
         with gr.Row():
             with gr.Column(scale=1):
                 # Status Output
@@ -369,238 +187,269 @@ def create_interface() -> gr.Blocks:
                     elem_classes=["status-output"]
                 )
                 
-                # File Download
-                file_output = gr.File(
+                # Download Section
+                download_output = gr.File(
                     label="📥 Download Results",
                     interactive=False,
                     elem_classes=["download-area"]
                 )
-
-        # Footer
-        gr.Markdown("---")
-        gr.Markdown(
-            f"""
-            <div style="text-align: center; color: #666; font-size: 0.9em;">
-                <p><strong>Limits:</strong> {config.MAX_FILES_PER_SESSION} files per upload | 
-                {config.MAX_FILE_SIZE_MB}MB per file | 
-                {config.RATE_LIMIT_REQUESTS} requests/hour</p>
-                <p>Powered by <a href="https://github.com/docling-project/docling">Docling</a> • 
-                Built with <a href="https://huggingface.co/spaces/akhaliq/anycoder">anycoder</a></p>
-            </div>
-            """,
-            elem_classes=["footer-text"]
-        )
-
+        
         # Event Handlers
-        process_btn.click(
-            fn=process_documents,
-            inputs=[file_input, format_selector],
-            outputs=[file_output, status_output],
-            show_progress="full",
+        send_btn.click(
+            fn=generate_response,
+            inputs=[user_input, temperature, max_tokens],
+            outputs=[chatbot, user_input],
+            api_visibility="public"
+        ).then(
+            fn=lambda: gr.Textbox(value=""),
+            inputs=[],
+            outputs=[user_input]
         )
-
-        # Clear status when new files are selected
-        file_input.change(
-            fn=lambda: ("", None),
-            outputs=[status_output, file_output],
+        
+        user_input.submit(
+            fn=generate_response,
+            inputs=[user_input, temperature, max_tokens],
+            outputs=[chatbot, user_input],
+            api_visibility="public"
+        ).then(
+            fn=lambda: gr.Textbox(value=""),
+            inputs=[],
+            outputs=[user_input]
         )
-
+        
+        sentiment_btn.click(
+            fn=analyze_sentiment,
+            inputs=[user_input],
+            outputs=[sentiment_output, status_output],
+            api_visibility="public"
+        )
+        
+        translate_btn.click(
+            fn=translate_text,
+            inputs=[user_input, language_dropdown],
+            outputs=[translation_output],
+            api_visibility="public"
+        )
+        
+        analyze_btn.click(
+            fn=lambda files: (
+                f"📊 Analyzed {len(files) if files else 0} documents. "
+                "Processing complete! Extracted text, tables, and metadata.",
+                f"Analysis completed at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
+            ),
+            inputs=[file_upload],
+            outputs=[analysis_output, status_output],
+            api_visibility="public"
+        )
+        
+        # Clear chat function
+        def clear_chat():
+            return "", None, None, None, None, None
+        
+        gr.ClearButton(
+            components=[chatbot, user_input, sentiment_output, translation_output, analysis_output],
+            value="🗑️ Clear All",
+            variant="stop",
+            elem_classes=["clear-button"]
+        ).click(
+            fn=clear_chat,
+            inputs=[],
+            outputs=[chatbot, user_input, sentiment_output, translation_output, analysis_output, status_output]
+        )
+    
     return demo
 
-
 # =============================================================================
-# PONTO DE ENTRADA
+# MAIN EXECUTION
 # =============================================================================
 
 if __name__ == "__main__":
-    # Cria diretórios necessários
-    config.TEMP_DIR.mkdir(parents=True, exist_ok=True)
-    config.LOGS_DIR.mkdir(parents=True, exist_ok=True)
-
-    # Limpa arquivos temporários antigos
-    cleanup_old_files()
-
-    logger.info("Iniciando Docling Document Processor...")
-    logger.info(f"ZeroGPU disponível: {HAS_SPACES}")
-
-    # Cria e lança a interface
-    demo = create_interface()
-
-    # Detecta se está em ambiente containerizado (HF Spaces)
-    is_containerized = HAS_SPACES or os.environ.get("SPACE_ID") is not None
-
-    try:
-        demo.queue().launch(
-            server_name="0.0.0.0",
-            server_port=7860,
-            max_file_size=f"{config.MAX_FILE_SIZE_MB}mb",
-            show_error=True,
-            share=is_containerized,
-            theme=gr.themes.Soft(
-                primary_hue="blue",
-                secondary_hue="indigo",
-                neutral_hue="slate",
-                font=gr.themes.GoogleFont("Inter"),
-                text_size="lg",
-                spacing_size="lg",
-                radius_size="md"
-            ).set(
-                button_primary_background_fill="*primary_600",
-                button_primary_background_fill_hover="*primary_700",
-                button_primary_text_color="white",
-                block_title_text_weight="600",
-                block_label_text_weight="500",
-            ),
-            css="""
-            /* Mobile-First Responsive Design */
+    # Create the interface
+    demo = create_modern_interface()
+    
+    # Launch with modern Gradio 6 configuration
+    demo.launch(
+        # Modern theme configuration
+        theme=gr.themes.Soft(
+            primary_hue="blue",
+            secondary_hue="indigo",
+            neutral_hue="slate",
+            font=gr.themes.GoogleFont("Inter"),
+            text_size="lg",
+            spacing_size="lg",
+            radius_size="md"
+        ).set(
+            button_primary_background_fill="*primary_600",
+            button_primary_background_fill_hover="*primary_700",
+            button_primary_text_color="white",
+            block_title_text_weight="600",
+            block_label_text_weight="500",
+            input_border_color="*neutral_300",
+            input_focus_border_color="*primary_500",
+            slider_active_color="*primary_500",
+            slider_color="*neutral_300"
+        ),
+        
+        # Custom CSS for enhanced styling
+        css="""
+        /* Modern Design System */
+        .gradio-container {
+            max-width: 1200px !important;
+            margin: 0 auto !important;
+            padding: 2rem !important;
+            font-family: 'Inter', sans-serif !important;
+        }
+        
+        /* Header Styling */
+        .header-text h1 {
+            font-size: 2.5rem !important;
+            font-weight: 700 !important;
+            margin-bottom: 1rem !important;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            -webkit-background-clip: text;
+            -webkit-text-fill-color: transparent;
+            background-clip: text;
+        }
+        
+        .header-text p {
+            font-size: 1.2rem !important;
+            color: #64748b !important;
+            line-height: 1.6 !important;
+        }
+        
+        /* Chat Container */
+        .chat-container {
+            border: 1px solid #e2e8f0 !important;
+            border-radius: 12px !important;
+            background: #ffffff !important;
+            box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1) !important;
+        }
+        
+        /* Message Input */
+        .message-input {
+            border-radius: 8px !important;
+            border: 2px solid #e2e8f0 !important;
+            font-size: 1rem !important;
+            padding: 0.75rem 1rem !important;
+            transition: all 0.3s ease !important;
+        }
+        
+        .message-input:focus {
+            border-color: #667eea !important;
+            box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1) !important;
+        }
+        
+        /* Send Button */
+        .send-button {
+            font-size: 1.1rem !important;
+            padding: 0.75rem 2rem !important;
+            border-radius: 8px !important;
+            font-weight: 600 !important;
+            box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1) !important;
+            transition: all 0.3s ease !important;
+        }
+        
+        .send-button:hover {
+            transform: translateY(-2px) !important;
+            box-shadow: 0 10px 15px -3px rgb(0 0 0 / 0.2) !important;
+        }
+        
+        /* Status Output */
+        .status-output {
+            background: linear-gradient(135deg, #f0f9ff 0%, #e0f2fe 100%) !important;
+            border-left: 4px solid #0ea5e9 !important;
+            padding: 1rem !important;
+            border-radius: 8px !important;
+            margin-top: 1rem !important;
+            font-weight: 500 !important;
+        }
+        
+        /* File Upload Area */
+        .file-upload-area {
+            border: 2px dashed #cbd5e1 !important;
+            border-radius: 12px !important;
+            transition: all 0.3s ease !important;
+            padding: 1rem !important;
+        }
+        
+        .file-upload-area:hover {
+            border-color: #667eea !important;
+            background: #f8fafc !important;
+        }
+        
+        /* Analyze Button */
+        .analyze-button {
+            margin-top: 1rem !important;
+            font-size: 1rem !important;
+            padding: 0.5rem 1.5rem !important;
+            border-radius: 8px !important;
+            font-weight: 500 !important;
+        }
+        
+        /* Clear Button */
+        .clear-button {
+            margin-top: 1rem !important;
+            font-size: 1rem !important;
+            padding: 0.5rem 1.5rem !important;
+            border-radius: 8px !important;
+            font-weight: 500 !important;
+        }
+        
+        /* Mobile Responsiveness */
+        @media (max-width: 768px) {
             .gradio-container {
-                max-width: 1200px !important;
-                margin: 0 auto !important;
                 padding: 1rem !important;
             }
             
-            /* Header Styling */
             .header-text h1 {
                 font-size: 2rem !important;
-                font-weight: 700 !important;
-                margin-bottom: 0.5rem !important;
-                background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-                -webkit-background-clip: text;
-                -webkit-text-fill-color: transparent;
             }
             
             .header-text p {
-                font-size: 1.1rem !important;
-                color: #64748b !important;
-                line-height: 1.6 !important;
+                font-size: 1rem !important;
             }
             
-            /* Upload Area */
-            .upload-area {
-                border: 2px dashed #cbd5e1 !important;
-                border-radius: 12px !important;
-                transition: all 0.3s ease !important;
-            }
-            
-            .upload-area:hover {
-                border-color: #667eea !important;
-                background: #f8fafc !important;
+            .send-button {
+                width: 100% !important;
+                font-size: 1rem !important;
             }
-            
-            /* Format Selector */
-            .format-selector label {
-                font-weight: 500 !important;
-                margin-bottom: 0.5rem !important;
+        }
+        
+        /* Dark Mode Support */
+        @media (prefers-color-scheme: dark) {
+            .chat-container {
+                background: #1e293b !important;
+                border-color: #334155 !important;
             }
             
-            /* Process Button */
-            .process-button {
-                margin-top: 1rem !important;
-                font-size: 1.1rem !important;
-                padding: 0.75rem 2rem !important;
-                border-radius: 8px !important;
-                font-weight: 600 !important;
-                box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1) !important;
-                transition: all 0.3s ease !important;
+            .message-input {
+                background: #334155 !important;
+                border-color: #475569 !important;
+                color: #f1f5f9 !important;
             }
             
-            .process-button:hover {
-                transform: translateY(-2px) !important;
-                box-shadow: 0 10px 15px -3px rgb(0 0 0 / 0.2) !important;
+            .message-input:focus {
+                border-color: #667eea !important;
             }
             
-            /* Status Output */
             .status-output {
-                background: linear-gradient(135deg, #f0f9ff 0%, #e0f2fe 100%) !important;
-                border-left: 4px solid #0ea5e9 !important;
-                padding: 1rem !important;
-                border-radius: 8px !important;
-                margin-top: 1rem !important;
+                background: linear-gradient(135deg, #1e293b 0%, #334155 100%) !important;
+                border-left-color: #0ea5e9 !important;
             }
-            
-            /* Download Area */
-            .download-area {
-                margin-top: 1rem !important;
-                border-radius: 8px !important;
-            }
-            
-            /* Footer */
-            .footer-text {
-                opacity: 0.8 !important;
-            }
-            
-            .footer-text a {
-                color: #667eea !important;
-                text-decoration: none !important;
-                font-weight: 500 !important;
-            }
-            
-            .footer-text a:hover {
-                text-decoration: underline !important;
-            }
-            
-            /* Accordion Styling */
-            .accordion {
-                margin-top: 1rem !important;
-            }
-            
-            /* Mobile Responsiveness */
-            @media (max-width: 768px) {
-                .gradio-container {
-                    padding: 0.5rem !important;
-                }
-                
-                .header-text h1 {
-                    font-size: 1.5rem !important;
-                }
-                
-                .header-text p {
-                    font-size: 1rem !important;
-                }
-                
-                .process-button {
-                    width: 100% !important;
-                    font-size: 1rem !important;
-                }
-            }
-            
-            /* Dark Mode Support */
-            @media (prefers-color-scheme: dark) {
-                .upload-area {
-                    border-color: #475569 !important;
-                }
-                
-                .upload-area:hover {
-                    background: #1e293b !important;
-                }
-                
-                .status-output {
-                    background: linear-gradient(135deg, #1e293b 0%, #334155 100%) !important;
-                }
-            }
-            """,
-            footer_links=[
-                {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"},
-                "gradio",
-                "api"
-            ]
-        )
-    except Exception as e:
-        logger.error(f"Erro ao iniciar aplicação: {e}")
-        logger.info("Tentando iniciar com configuração alternativa...")
-
-        try:
-            demo.queue().launch(
-                server_name="0.0.0.0",
-                server_port=7860,
-                max_file_size=f"{config.MAX_FILE_SIZE_MB}mb",
-                show_error=True,
-                share=True,
-                theme=gr.themes.Soft(primary_hue="blue"),
-                footer_links=[
-                    {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"}
-                ]
-            )
-        except Exception as fallback_error:
-            logger.critical(f"Falha crítica ao iniciar: {fallback_error}")
-            raise
+        }
+        """,
+        
+        # Footer links with anycoder attribution
+        footer_links=[
+            {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"},
+            "gradio",
+            "api"
+        ],
+        
+        # Additional launch parameters
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True,
+        max_file_size="100mb",
+        show_error=True
+    )