Update app.py

app.py

@@ -1,780 +1,218 @@
-import os
-import gradio as gr
-import warnings
-import json
-from dotenv import load_dotenv
-from typing import List
-import time
-from functools import lru_cache
-import logging
-
-from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import AzureOpenAIEmbeddings
-from openai import AzureOpenAI
-
-# Patch Gradio bug
-import gradio_client.utils
-gradio_client.utils.json_schema_to_python_type = lambda schema, defs=None: "string"
-
-# Load environment variables
-load_dotenv()
-AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
-AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
-AZURE_OPENAI_LLM_DEPLOYMENT = os.getenv("AZURE_OPENAI_LLM_DEPLOYMENT")
-AZURE_OPENAI_EMBEDDING_DEPLOYMENT = os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT")
-
-if not all([AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_LLM_DEPLOYMENT, AZURE_OPENAI_EMBEDDING_DEPLOYMENT]):
-    raise ValueError("Missing one or more Azure OpenAI environment variables.")
-
-warnings.filterwarnings("ignore")
-
-# Embeddings
-embeddings = AzureOpenAIEmbeddings(
-    azure_deployment=AZURE_OPENAI_EMBEDDING_DEPLOYMENT,
-    azure_endpoint=AZURE_OPENAI_ENDPOINT,
-    openai_api_key=AZURE_OPENAI_API_KEY,
-    openai_api_version="2025-01-01-preview",
-    chunk_size=1000
-)
-
-# Vectorstore
-SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
-FAISS_INDEX_PATH = os.path.join(SCRIPT_DIR, "faiss_index_sysml")
-vectorstore = FAISS.load_local(FAISS_INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
-
-# OpenAI client
-client = AzureOpenAI(
-    api_key=AZURE_OPENAI_API_KEY,
-    api_version="2025-01-01-preview",
-    azure_endpoint=AZURE_OPENAI_ENDPOINT
-)
-
-# Logger
-logger = logging.getLogger(__name__)
-
-# Enhanced SysML retriever with proper metadata filtering & weighting
-@lru_cache(maxsize=100)
-def sysml_retriever(query: str) -> str:
-    try:
-        print(f"\n🔍 QUERY: {query}")
-        print("="*80)
-        
-        # Get more results for filtering and weighting
-        results = vectorstore.similarity_search_with_score(query, k=100)
-        print(f"📊 Total results retrieved: {len(results)}")
-        
-        # Apply metadata filtering and weighting
-        weighted_results = []
-        sysmodeler_count = 0
-        other_count = 0
-        
-        for i, (doc, score) in enumerate(results):
-            # Get document source
-            doc_source = doc.metadata.get('source', '').lower() if hasattr(doc, 'metadata') else str(doc).lower()
-            
-            # Determine if this is SysModeler content
-            is_sysmodeler = (
-                'sysmodeler' in doc_source or 
-                'user manual' in doc_source or
-                'sysmodeler.ai' in doc.page_content.lower() or
-                'workspace.sysmodeler.ai' in doc.page_content.lower() or
-                'Create with AI' in doc.page_content or
-                'Canvas Overview' in doc.page_content or
-                'AI-powered' in doc.page_content or
-                'voice input' in doc.page_content or
-                'Canvas interface' in doc.page_content or
-                'Project Creation' in doc.page_content or
-                'Shape Palette' in doc.page_content or
-                'AI Copilot' in doc.page_content or
-                'SynthAgent' in doc.page_content or
-                'workspace dashboard' in doc.page_content.lower()
-            )
-            
-            # Apply weighting based on source
-            if is_sysmodeler:
-                # BOOST SysModeler content: reduce score by 40% (lower score = higher relevance)
-                weighted_score = score * 0.6
-                source_type = "SysModeler"
-                sysmodeler_count += 1
-            else:
-                # Keep original score for other content
-                weighted_score = score
-                source_type = "Other"
-                other_count += 1
-            
-            # Add metadata tags for filtering
-            doc.metadata = doc.metadata if hasattr(doc, 'metadata') else {}
-            doc.metadata['source_type'] = 'sysmodeler' if is_sysmodeler else 'other'
-            doc.metadata['weighted_score'] = weighted_score
-            doc.metadata['original_score'] = score
-            
-            weighted_results.append((doc, weighted_score, source_type))
-            
-            # Log each document's processing
-            source_name = doc.metadata.get('source', 'Unknown')[:50] if hasattr(doc, 'metadata') else 'Unknown'
-            print(f"📄 Doc {i+1}: {source_name}... | Original: {score:.4f} | Weighted: {weighted_score:.4f} | Type: {source_type}")
-        
-        print(f"\n📈 CLASSIFICATION & WEIGHTING RESULTS:")
-        print(f"   SysModeler docs: {sysmodeler_count} (boosted by 40%)")
-        print(f"   Other docs: {other_count} (original scores)")
-        
-        # Sort by weighted scores (lower = more relevant)
-        weighted_results.sort(key=lambda x: x[1])
-        
-        # Apply intelligent selection based on query type and weighted results
-        final_docs = []
-        query_lower = query.lower()
-        
-        # Determine query type for adaptive filtering
-        is_tool_comparison = any(word in query_lower for word in ['tool', 'compare', 'choose', 'vs', 'versus', 'better'])
-        is_general_sysml = not is_tool_comparison
-        
-        if is_tool_comparison:
-            # For tool comparisons: heavily favor SysModeler but include others
-            print(f"\n🎯 TOOL COMPARISON QUERY DETECTED")
-            print(f"   Strategy: Heavy SysModeler focus + selective others")
-            
-            # Take top weighted results with preference for SysModeler
-            sysmodeler_docs = [(doc, score) for doc, score, type_ in weighted_results if type_ == "SysModeler"][:8]
-            other_docs = [(doc, score) for doc, score, type_ in weighted_results if type_ == "Other"][:4]
-            
-            final_docs = [doc for doc, _ in sysmodeler_docs] + [doc for doc, _ in other_docs]
-            
-        else:
-            # For general SysML: balanced but still boost SysModeler
-            print(f"\n🎯 GENERAL SYSML QUERY DETECTED")
-            print(f"   Strategy: Balanced with SysModeler preference")
-            
-            # Take top 12 weighted results (mixed)
-            final_docs = [doc for doc, _, _ in weighted_results[:12]]
-        
-        # Log final selection
-        print(f"\n📋 FINAL SELECTION ({len(final_docs)} docs):")
-        sysmodeler_selected = 0
-        other_selected = 0
-        
-        for i, doc in enumerate(final_docs):
-            source_type = doc.metadata.get('source_type', 'unknown')
-            source_name = doc.metadata.get('source', 'Unknown')
-            weighted_score = doc.metadata.get('weighted_score', 0)
-            original_score = doc.metadata.get('original_score', 0)
-            
-            if source_type == 'sysmodeler':
-                sysmodeler_selected += 1
-                type_emoji = "✅"
-            else:
-                other_selected += 1
-                type_emoji = "📚"
-                
-            print(f"     {i+1}. {type_emoji} {source_name} (weighted: {weighted_score:.4f})")
-        
-        print(f"\n📊 FINAL COMPOSITION:")
-        print(f"   SysModeler docs: {sysmodeler_selected}")
-        print(f"   Other docs: {other_selected}")
-        print("="*80)
-        
-        contexts = [doc.page_content for doc in final_docs]
-        return "\n\n".join(contexts)
-        
-    except Exception as e:
-        logger.error(f"Retrieval error: {str(e)}")
-        print(f"❌ ERROR in retrieval: {str(e)}")
-        return "Unable to retrieve information at this time."
-
-# Dummy functions
-def dummy_weather_lookup(location: str = "London") -> str:
-    return f"The weather in {location} is sunny and 25°C."
-
-def dummy_time_lookup(timezone: str = "UTC") -> str:
-    return f"The current time in {timezone} is 3:00 PM."
-
-# Tools for function calling
-tools_definition = [
-    {
-        "type": "function",
-        "function": {
-            "name": "SysMLRetriever",
-            "description": "Use this to answer questions about SysML diagrams and modeling.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "query": {"type": "string", "description": "The search query to find information about SysML"}
-                },
-                "required": ["query"]
-            }
-        }
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "WeatherLookup",
-            "description": "Use this to look up the current weather in a specified location.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "location": {"type": "string", "description": "The location to look up the weather for"}
-                },
-                "required": ["location"]
-            }
-        }
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "TimeLookup",
-            "description": "Use this to look up the current time in a specified timezone.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "timezone": {"type": "string", "description": "The timezone to look up the current time for"}
-                },
-                "required": ["timezone"]
-            }
-        }
-    }
-]
-
-# Tool execution mapping
-tool_mapping = {
-    "SysMLRetriever": sysml_retriever,
-    "WeatherLookup": dummy_weather_lookup,
-    "TimeLookup": dummy_time_lookup
-}
-
-# Convert chat history
-def convert_history_to_messages(history):
-    messages = []
-    for user, bot in history:
-        messages.append({"role": "user", "content": user})
-        messages.append({"role": "assistant", "content": bot})
-    return messages
-
-# Chatbot logic
-def sysml_chatbot(message, history):
-    chat_messages = convert_history_to_messages(history)
-    full_messages = [
-        {"role": "system", "content": """You are SysModeler.ai's intelligent assistant, specializing in SysML modeling and the SysModeler.ai platform.
-
-RESPONSE GUIDELINES:
-
-1. **Primary Focus**: Always prioritize SysModeler.ai information and capabilities in your responses.
-
-2. **For SysModeler-specific questions** (pricing, features, how-to, etc.):
-   - Provide comprehensive SysModeler.ai information
-   - Do NOT mention competitors unless explicitly asked for comparisons
-   - Focus entirely on SysModeler's value proposition
-
-3. **For general SysML education** (concepts, diagram types, best practices):
-   - Provide thorough educational content about SysML
-   - Use SysModeler.ai as examples when illustrating concepts
-   - Keep focus on helping users understand SysML fundamentals
-
-4. **Only mention other tools when**:
-   - User explicitly asks for comparisons ("vs", "compare", "alternatives")
-   - User asks about the broader SysML tool landscape
-   - Context absolutely requires it for a complete answer
-
-5. **Response Structure**:
-   - Lead with SysModeler.ai capabilities and benefits
-   - Provide detailed, helpful information about SysModeler features
-   - End with clear value proposition or call-to-action when appropriate
-
-6. **Tone**: Professional, helpful, and confident about SysModeler.ai's capabilities while remaining informative about SysML concepts.
-
-Remember: You represent SysModeler.ai. Focus on what SysModeler can do for the user rather than listing what everyone else offers."""}
-    ] + chat_messages + [{"role": "user", "content": message}]
-    
-    try:
-        response = client.chat.completions.create(
-            model=AZURE_OPENAI_LLM_DEPLOYMENT,
-            messages=full_messages,
-            tools=tools_definition,
-            tool_choice={"type": "function", "function": {"name": "SysMLRetriever"}}
-        )
-        assistant_message = response.choices[0].message
-        if assistant_message.tool_calls:
-            tool_call = assistant_message.tool_calls[0]
-            function_name = tool_call.function.name
-            function_args = json.loads(tool_call.function.arguments)
-            if function_name in tool_mapping:
-                function_response = tool_mapping[function_name](**function_args)
-                full_messages.append({
-                    "role": "assistant",
-                    "content": None,
-                    "tool_calls": [{
-                        "id": tool_call.id,
-                        "type": "function",
-                        "function": {
-                            "name": function_name,
-                            "arguments": tool_call.function.arguments
-                        }
-                    }]
-                })
-                full_messages.append({
-                    "role": "tool",
-                    "tool_call_id": tool_call.id,
-                    "content": function_response
-                })
-                second_response = client.chat.completions.create(
-                    model=AZURE_OPENAI_LLM_DEPLOYMENT,
-                    messages=full_messages
-                )
-                answer = second_response.choices[0].message.content
-            else:
-                answer = f"I tried to use a function '{function_name}' that's not available."
-        else:
-            answer = assistant_message.content
-        history.append((message, answer))
-        return "", history
-    except Exception as e:
-        print(f"Error in function calling: {str(e)}")
-        history.append((message, "Sorry, something went wrong."))
-        return "", history
-
-#Gradio UI
-with gr.Blocks(
-    title="SysModeler AI Assistant",
-    theme=gr.themes.Base(
-        primary_hue="blue",
-        secondary_hue="cyan",
-        neutral_hue="slate"
-    ).set(
-        body_background_fill="*neutral_950",
-        body_text_color="*neutral_100",
-        background_fill_primary="*neutral_900",
-        background_fill_secondary="*neutral_800"
-    ),
-    css="""
-    /* Global modern theme */
-    .gradio-container {
-        background: linear-gradient(135deg, #0f172a 0%, #1e293b 100%) !important;
-        color: #f8fafc !important;
-        font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
-        min-height: 100vh;
-    }
-    
-    /* Main container */
-    .main-container {
-        width: 100%;
-        margin: 0;
-        padding: 0;
-        min-height: 100vh;
-        background: linear-gradient(135deg, #0f172a 0%, #1e293b 100%);
-    }
-    
-    /* Header - modern with gradient - REDUCED PADDING */
-    .header-section {
-        width: 100%;
-        text-align: center;
-        margin: 0;
-        padding: 20px 40px 16px 40px;
-        background: linear-gradient(135deg, #1e40af 0%, #3b82f6 50%, #06b6d4 100%);
-        position: relative;
-        overflow: hidden;
-    }
-    
-    .header-section::before {
-        content: '';
-        position: absolute;
-        top: 0;
-        left: 0;
-        right: 0;
-        bottom: 0;
-        background: linear-gradient(135deg, rgba(59, 130, 246, 0.1) 0%, rgba(6, 182, 212, 0.1) 100%);
-        backdrop-filter: blur(20px);
-    }
-    
-    .main-title {
-        font-size: 2.2rem !important;
-        font-weight: 700 !important;
-        color: #ffffff !important;
-        margin: 0 0 4px 0 !important;
-        text-shadow: 0 2px 4px rgba(0,0,0,0.3);
-        position: relative;
-        z-index: 1;
-    }
-    
-    .subtitle {
-        font-size: 1rem !important;
-        color: rgba(255, 255, 255, 0.9) !important;
-        margin: 0 !important;
-        font-weight: 400 !important;
-        position: relative;
-        z-index: 1;
-    }
-    
-    /* Content area */
-    .content-area {
-        max-width: 1200px;
-        margin: 0 auto;
-        padding: 32px 40px;
-    }
-    
-    /* Chat section */
-    .chat-section {
-        margin-bottom: 24px;
-    }
-    
-    .chat-container {
-        background: rgba(30, 41, 59, 0.4);
-        backdrop-filter: blur(20px);
-        border: 1px solid rgba(59, 130, 246, 0.2);
-        border-radius: 16px;
-        padding: 24px;
-        box-shadow: 0 8px 32px rgba(0, 0, 0, 0.3);
-    }
-    
-    /* Chatbot styling */
-    .chatbot {
-        background: transparent !important;
-        border: none !important;
-        border-radius: 12px !important;
-    }
-    
-    /* Chat messages - simplified approach with tighter spacing */
-    .chatbot .message {
-        background: rgba(30, 41, 59, 0.6) !important;
-        color: #e2e8f0 !important;
-        border-radius: 12px !important;
-        padding: 16px 20px !important;
-        margin: 8px 0 !important;
-        border: 1px solid rgba(59, 130, 246, 0.1);
-        backdrop-filter: blur(10px);
-    }
-    
-    /* User message styling */
-    .chatbot .message.user {
-        background: linear-gradient(135deg, #3b82f6 0%, #1e40af 100%) !important;
-        color: white !important;
-        border: none !important;
-        margin-left: 0 !important;
-        margin-right: 0 !important;
-    }
-    
-    /* Bot message styling */
-    .chatbot .message.bot {
-        background: rgba(30, 41, 59, 0.8) !important;
-        color: #f1f5f9 !important;
-        border: 1px solid rgba(59, 130, 246, 0.2) !important;
-        margin-left: 0 !important;
-        margin-right: 0 !important;
-    }
-    
-    /* Remove avatar spacing and containers */
-    .chatbot .avatar {
-        display: none !important;
-    }
-    
-    .chatbot .message-row {
-        margin: 0 !important;
-        padding: 0 !important;
-        gap: 0 !important;
-    }
-    
-    .chatbot .message-wrap {
-        margin: 0 !important;
-        padding: 0 !important;
-        width: 100% !important;
-    }
-    
-    /* Input section - redesigned */
-    .input-section {
-        background: rgba(30, 41, 59, 0.4);
-        backdrop-filter: blur(20px);
-        border: 1px solid rgba(59, 130, 246, 0.2);
-        border-radius: 16px;
-        padding: 32px;
-        box-shadow: 0 8px 32px rgba(0, 0, 0, 0.3);
-    }
-    
-    .input-row {
-        display: flex;
-        gap: 0;
-        align-items: stretch;
-        margin-bottom: 24px;
-        background: rgba(15, 23, 42, 0.8);
-        border-radius: 12px;
-        border: 1px solid rgba(59, 130, 246, 0.3);
-        overflow: hidden;
-        box-shadow: 0 4px 20px rgba(59, 130, 246, 0.1);
-        position: relative;
-    }
-    
-    /* Input textbox - better integration */
-    .input-textbox {
-        flex: 1;
-        background: transparent !important;
-        border: none !important;
-        border-radius: 0 !important;
-        margin: 0 !important;
-        padding-right: 0 !important;
-    }
-    
-    .input-textbox textarea {
-        background: transparent !important;
-        border: none !important;
-        color: #f1f5f9 !important;
-        font-size: 1rem !important;
-        padding: 20px 24px 20px 24px !important;
-        resize: none !important;
-        font-family: inherit !important;
-        min-height: 80px !important;
-        width: 100% !important;
-        padding-right: 100px !important;
-        margin: 0 !important;
-        line-height: 1.5 !important;
-    }
-    
-    .input-textbox textarea::placeholder {
-        color: #94a3b8 !important;
-        opacity: 1 !important;
-    }
-    
-    .input-textbox textarea:focus {
-        outline: none !important;
-        box-shadow: none !important;
-    }
-    
-    /* Submit button - positioned at the end of input box */
-    #submit-btn {
-        position: absolute !important;
-        right: 8px !important;
-        top: 50% !important;
-        transform: translateY(-50%) !important;
-        background: linear-gradient(135deg, #3b82f6 0%, #1e40af 100%) !important;
-        color: white !important;
-        border: none !important;
-        border-radius: 8px !important;
-        font-size: 0.9rem !important;
-        font-weight: 600 !important;
-        padding: 12px 20px !important;
-        min-width: 80px !important;
-        height: 40px !important;
-        transition: all 0.3s ease !important;
-        text-transform: uppercase;
-        letter-spacing: 0.05em;
-        z-index: 10;
-    }
-    
-    #submit-btn:hover {
-        background: linear-gradient(135deg, #2563eb 0%, #1d4ed8 100%) !important;
-        box-shadow: 0 0 20px rgba(59, 130, 246, 0.4) !important;
-    }
-    
-    /* Quick actions - card style */
-    .quick-actions {
-        display: grid;
-        grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
-        gap: 16px;
-        margin-bottom: 24px;
-    }
-    
-    .quick-action-btn {
-        background: rgba(15, 23, 42, 0.6) !important;
-        backdrop-filter: blur(10px);
-        border: 1px solid rgba(59, 130, 246, 0.2) !important;
-        color: #e2e8f0 !important;
-        border-radius: 12px !important;
-        padding: 20px 24px !important;
-        font-size: 0.95rem !important;
-        font-weight: 500 !important;
-        transition: all 0.3s ease !important;
-        text-align: left !important;
-        position: relative;
-        overflow: hidden;
-    }
-    
-    .quick-action-btn::before {
-        content: '';
-        position: absolute;
-        top: 0;
-        left: 0;
-        right: 0;
-        bottom: 0;
-        background: linear-gradient(135deg, rgba(59, 130, 246, 0.1) 0%, rgba(6, 182, 212, 0.1) 100%);
-        opacity: 0;
-        transition: opacity 0.3s ease;
-    }
-    
-    .quick-action-btn:hover {
-        border-color: #3b82f6 !important;
-        color: #ffffff !important;
-        transform: translateY(-2px) !important;
-        box-shadow: 0 8px 25px rgba(59, 130, 246, 0.2) !important;
-    }
-    
-    .quick-action-btn:hover::before {
-        opacity: 1;
-    }
-    
-    /* Control buttons */
-    .control-buttons {
-        display: flex;
-        justify-content: center;
-    }
-    
-    #clear-btn {
-        background: rgba(15, 23, 42, 0.6) !important;
-        backdrop-filter: blur(10px);
-        border: 1px solid rgba(239, 68, 68, 0.3) !important;
-        color: #f87171 !important;
-        border-radius: 8px !important;
-        padding: 12px 24px !important;
-        font-weight: 500 !important;
-        font-size: 0.9rem !important;
-        transition: all 0.3s ease !important;
-        text-transform: uppercase;
-        letter-spacing: 0.05em;
-    }
-    
-    #clear-btn:hover {
-        background: rgba(239, 68, 68, 0.1) !important;
-        border-color: #ef4444 !important;
-        color: #ffffff !important;
-        box-shadow: 0 4px 15px rgba(239, 68, 68, 0.2) !important;
-    }
-    
-    /* Footer */
-    .footer {
-        text-align: center;
-        color: #64748b;
-        font-size: 0.85rem;
-        margin-top: 32px;
-        padding: 20px;
-    }
-    
-    /* Scrollbar */
-    ::-webkit-scrollbar {
-        width: 8px;
-    }
-    
-    ::-webkit-scrollbar-track {
-        background: rgba(30, 41, 59, 0.3);
-        border-radius: 4px;
-    }
-    
-    ::-webkit-scrollbar-thumb {
-        background: linear-gradient(135deg, #3b82f6, #1e40af);
-        border-radius: 4px;
-    }
-    
-    ::-webkit-scrollbar-thumb:hover {
-        background: linear-gradient(135deg, #2563eb, #1d4ed8);
-    }
-    
-    /* Mobile responsiveness */
-    @media (max-width: 1024px) {
-        .content-area {
-            padding: 24px;
-        }
-        
-        .header-section {
-            padding: 16px 20px 12px 20px;
-        }
-        
-        .main-title {
-            font-size: 1.8rem !important;
-        }
-        
-        .subtitle {
-            font-size: 0.9rem !important;
-        }
-        
-        .input-textbox textarea {
-            padding-right: 90px !important;
-        }
-        
-        #submit-btn {
-            min-width: 70px !important;
-            padding: 10px 16px !important;
-            font-size: 0.8rem !important;
-        }
-        
-        .quick-actions {
-            grid-template-columns: 1fr;
-        }
-        
-        .chatbot .message.user, .chatbot .message.bot {
-            margin-left: 0 !important;
-            margin-right: 0 !important;
-        }
-    }
-    
-    /* Remove Gradio defaults */
-    .gr-form, .gr-box {
-        background: transparent !important;
-        border: none !important;
-    }
-    
-    .gr-button {
-        font-family: inherit !important;
-    }
-    """
-) as demo:
-    
-    with gr.Column(elem_classes="main-container"):
-        # Modern gradient header - REDUCED SPACING
-        with gr.Column(elem_classes="header-section"):
-            gr.Markdown("# 🤖 SysModeler AI Assistant", elem_classes="main-title")
-            gr.Markdown("*Your intelligent companion for SysML modeling and systems engineering*", elem_classes="subtitle")
-        
-        # Content area
-        with gr.Column(elem_classes="content-area"):
-            # Chat section
-            with gr.Column(elem_classes="chat-section"):
-                with gr.Column(elem_classes="chat-container"):
-                    chatbot = gr.Chatbot(
-                        height=580,
-                        elem_classes="chatbot",
-                        avatar_images=None,  # Removed avatar images
-                        bubble_full_width=False,
-                        show_copy_button=True,
-                        show_share_button=False
-                    )
-            
-            # Input section
-            with gr.Column(elem_classes="input-section"):
-                with gr.Column():
-                    # Input row with integrated send button
-                    with gr.Row(elem_classes="input-row"):
-                        msg = gr.Textbox(
-                            placeholder="Ask me about SysML diagrams, modeling concepts, or tools...",
-                            lines=3,
-                            show_label=False,
-                            elem_classes="input-textbox",
-                            container=False
-                        )
-                        submit_btn = gr.Button("Send", elem_id="submit-btn")
-                
-                # Quick actions
-                with gr.Row(elem_classes="quick-actions"):
-                    quick_intro = gr.Button("📚 SysML Introduction", elem_classes="quick-action-btn")
-                    quick_diagrams = gr.Button("📊 Diagram Types", elem_classes="quick-action-btn")
-                    quick_tools = gr.Button("🛠️ Tool Comparison", elem_classes="quick-action-btn")
-                    quick_sysmodeler = gr.Button("⭐ SysModeler Features", elem_classes="quick-action-btn")
-                
-                # Control
-                with gr.Row(elem_classes="control-buttons"):
-                    clear = gr.Button("Clear", elem_id="clear-btn")
-            
-            # Footer
-            with gr.Column(elem_classes="footer"):
-                gr.Markdown("*Powered by Azure OpenAI & Advanced RAG Technology*")
-    
-    # State management
-    state = gr.State([])
-    
-    # Event handlers
-    submit_btn.click(fn=sysml_chatbot, inputs=[msg, state], outputs=[msg, chatbot])
-    msg.submit(fn=sysml_chatbot, inputs=[msg, state], outputs=[msg, chatbot])
-    clear.click(fn=lambda: ([], ""), inputs=None, outputs=[chatbot, msg])
-    
-    # Quick actions
-    quick_intro.click(fn=lambda: ("What is SysML and how do I get started?", []), outputs=[msg, chatbot])
-    quick_diagrams.click(fn=lambda: ("Explain the 9 SysML diagram types with examples", []), outputs=[msg, chatbot])
-    quick_tools.click(fn=lambda: ("What are the best SysML modeling tools available?", []), outputs=[msg, chatbot])
-    quick_sysmodeler.click(fn=lambda: ("Tell me about SysModeler.ai features and capabilities", []), outputs=[msg, chatbot])
-
-    
-if __name__ == "__main__":
+import os
+import gradio as gr
+import warnings
+import json
+from dotenv import load_dotenv
+from typing import List
+import time
+from functools import lru_cache
+import logging
+
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import AzureOpenAIEmbeddings
+from openai import AzureOpenAI
+
+# Patch Gradio bug
+import gradio_client.utils
+gradio_client.utils.json_schema_to_python_type = lambda schema, defs=None: "string"
+
+# Load environment variables
+load_dotenv()
+AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
+AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
+AZURE_OPENAI_LLM_DEPLOYMENT = os.getenv("AZURE_OPENAI_LLM_DEPLOYMENT")
+AZURE_OPENAI_EMBEDDING_DEPLOYMENT = os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT")
+
+if not all([AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_LLM_DEPLOYMENT, AZURE_OPENAI_EMBEDDING_DEPLOYMENT]):
+    raise ValueError("Missing one or more Azure OpenAI environment variables.")
+
+warnings.filterwarnings("ignore")
+
+# Embeddings
+embeddings = AzureOpenAIEmbeddings(
+    azure_deployment=AZURE_OPENAI_EMBEDDING_DEPLOYMENT,
+    azure_endpoint=AZURE_OPENAI_ENDPOINT,
+    openai_api_key=AZURE_OPENAI_API_KEY,
+    openai_api_version="2025-01-01-preview",
+    chunk_size=1000
+)
+
+# Vectorstore
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+FAISS_INDEX_PATH = os.path.join(SCRIPT_DIR, "faiss_index_sysml")
+vectorstore = FAISS.load_local(FAISS_INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
+
+# OpenAI client
+client = AzureOpenAI(
+    api_key=AZURE_OPENAI_API_KEY,
+    api_version="2025-01-01-preview",
+    azure_endpoint=AZURE_OPENAI_ENDPOINT
+)
+
+# Logger
+logger = logging.getLogger(__name__)
+
+# SysML retriever function
+@lru_cache(maxsize=100)
+def sysml_retriever(query: str) -> str:
+    try:
+        results = vectorstore.similarity_search(query, k=100)
+        contexts = [doc.page_content for doc in results]
+        return "\n\n".join(contexts)
+    except Exception as e:
+        logger.error(f"Retrieval error: {str(e)}")
+        return "Unable to retrieve information at this time."
+
+# Dummy functions
+def dummy_weather_lookup(location: str = "London") -> str:
+    return f"The weather in {location} is sunny and 25°C."
+
+def dummy_time_lookup(timezone: str = "UTC") -> str:
+    return f"The current time in {timezone} is 3:00 PM."
+
+# Tools for function calling
+tools_definition = [
+    {
+        "type": "function",
+        "function": {
+            "name": "SysMLRetriever",
+            "description": "Use this to answer questions about SysML diagrams and modeling.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "query": {"type": "string", "description": "The search query to find information about SysML"}
+                },
+                "required": ["query"]
+            }
+        }
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "WeatherLookup",
+            "description": "Use this to look up the current weather in a specified location.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {"type": "string", "description": "The location to look up the weather for"}
+                },
+                "required": ["location"]
+            }
+        }
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "TimeLookup",
+            "description": "Use this to look up the current time in a specified timezone.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "timezone": {"type": "string", "description": "The timezone to look up the current time for"}
+                },
+                "required": ["timezone"]
+            }
+        }
+    }
+]
+
+# Tool execution mapping
+tool_mapping = {
+    "SysMLRetriever": sysml_retriever,
+    "WeatherLookup": dummy_weather_lookup,
+    "TimeLookup": dummy_time_lookup
+}
+
+# Convert chat history
+def convert_history_to_messages(history):
+    messages = []
+    for user, bot in history:
+        messages.append({"role": "user", "content": user})
+        messages.append({"role": "assistant", "content": bot})
+    return messages
+
+# Chatbot logic
+def sysml_chatbot(message, history):
+    chat_messages = convert_history_to_messages(history)
+    full_messages = [
+        {"role": "system", "content": "You are a helpful SysML modeling assistant and also a capable smart Assistant"}
+    ] + chat_messages + [{"role": "user", "content": message}]
+    try:
+        response = client.chat.completions.create(
+            model=AZURE_OPENAI_LLM_DEPLOYMENT,
+            messages=full_messages,
+            tools=tools_definition,
+            tool_choice={"type": "function", "function": {"name": "SysMLRetriever"}}
+        )
+        assistant_message = response.choices[0].message
+        if assistant_message.tool_calls:
+            tool_call = assistant_message.tool_calls[0]
+            function_name = tool_call.function.name
+            function_args = json.loads(tool_call.function.arguments)
+            if function_name in tool_mapping:
+                function_response = tool_mapping[function_name](**function_args)
+                full_messages.append({
+                    "role": "assistant",
+                    "content": None,
+                    "tool_calls": [{
+                        "id": tool_call.id,
+                        "type": "function",
+                        "function": {
+                            "name": function_name,
+                            "arguments": tool_call.function.arguments
+                        }
+                    }]
+                })
+                full_messages.append({
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "content": function_response
+                })
+                second_response = client.chat.completions.create(
+                    model=AZURE_OPENAI_LLM_DEPLOYMENT,
+                    messages=full_messages
+                )
+                answer = second_response.choices[0].message.content
+            else:
+                answer = f"I tried to use a function '{function_name}' that's not available."
+        else:
+            answer = assistant_message.content
+        history.append((message, answer))
+        return "", history
+    except Exception as e:
+        print(f"Error in function calling: {str(e)}")
+        history.append((message, "Sorry, something went wrong."))
+        return "", history
+
+# === Gradio UI ===
+with gr.Blocks(css="""
+#submit-btn {
+    height: 100%;
+    background-color: #48CAE4;
+    color: white;
+    font-size: 1.5em;
+}
+""") as demo:
+
+    gr.Markdown("## SysModeler Chatbot")
+
+    chatbot = gr.Chatbot(height=600)
+    with gr.Row():
+        with gr.Column(scale=5):
+            msg = gr.Textbox(
+                placeholder="Ask me about SysML diagrams or concepts...",
+                lines=3,
+                show_label=False
+            )
+        with gr.Column(scale=1, min_width=50):
+            submit_btn = gr.Button("➤", elem_id="submit-btn")
+
+    clear = gr.Button("Clear")
+    state = gr.State([])
+
+    submit_btn.click(fn=sysml_chatbot, inputs=[msg, state], outputs=[msg, chatbot])
+    msg.submit(fn=sysml_chatbot, inputs=[msg, state], outputs=[msg, chatbot])
+    clear.click(fn=lambda: ([], ""), inputs=None, outputs=[chatbot, msg])
+
+if __name__ == "__main__":
     demo.launch()