app.py

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()