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import os
import warnings
import gradio as gr
from dotenv import load_dotenv
from langchain.chains import ConversationalRetrievalChain
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import AzureOpenAIEmbeddings
from langchain_community.chat_models import AzureChatOpenAI
# 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_END_POINT_O3 = os.getenv("AZURE_END_POINT_O3")
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_END_POINT_O3,
AZURE_OPENAI_LLM_DEPLOYMENT,
AZURE_OPENAI_EMBEDDING_DEPLOYMENT]):
raise ValueError("Missing one or more Azure OpenAI environment variables.")
# Suppress warnings
warnings.filterwarnings("ignore")
# Initialize Azure embedding model
embeddings = AzureOpenAIEmbeddings(
azure_deployment=AZURE_OPENAI_EMBEDDING_DEPLOYMENT,
azure_endpoint=AZURE_OPENAI_ENDPOINT,
#azure_endpoint=AZURE_END_POINT_O3,
openai_api_key=AZURE_OPENAI_API_KEY,
openai_api_version="2025-01-01-preview", # updated to latest recommended version
chunk_size=1000
)
# Load FAISS vector store
vectorstore = FAISS.load_local(
"faiss_index_sysml", embeddings, allow_dangerous_deserialization=True
)
# Initialize Azure chat model
llm = AzureChatOpenAI(
deployment_name=AZURE_OPENAI_LLM_DEPLOYMENT,
azure_endpoint=AZURE_OPENAI_ENDPOINT,
#azure_endpoint=AZURE_END_POINT_O3,
openai_api_key=AZURE_OPENAI_API_KEY,
openai_api_version="2025-01-01-preview", # updated to latest recommended version
#temperature=0.5
)
# Build conversational RAG chain
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
return_source_documents=False
)
history = []
# Chatbot logic
def sysml_chatbot(message, history):
result = qa({"question": message, "chat_history": history})
answer = result["answer"]
history.append((message, answer))
return "", history
# Gradio UI
with gr.Blocks() 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("➤")
clear = gr.Button("Clear")
state = gr.State(history)
submit_btn.click(fn=sysml_chatbot, inputs=[msg, state], outputs=[msg, chatbot])
msg.submit(fn=sysml_chatbot, inputs=[msg, state], outputs=[msg, chatbot]) # still supports enter key
clear.click(fn=lambda: ([], ""), inputs=None, outputs=[chatbot, msg])
if __name__ == "__main__":
demo.launch()
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