Upload app.py

app.py

@@ -1,218 +1,780 @@
-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()
+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__":
+    demo.launch()