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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() |