app.py

import os
import gradio as gr
import warnings
import json
from dotenv import load_dotenv
import logging
from functools import lru_cache

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 = logging.getLogger(__name__)

def clean_em_dashes(text: str) -> str:
    text = text.replace("—which", ", which")
    text = text.replace("—that", ", that")
    text = text.replace("—no", ". No")
    text = text.replace("—and", ", and")
    text = text.replace("—but", ", but")
    text = text.replace("—so", ", so")
    text = text.replace("—you", ". You")
    text = text.replace("—it", ". It")
    text = text.replace("—just", ". Just")
    text = text.replace("—great", ", great")
    text = text.replace("—this", ". This")
    text = text.replace("—", ", ")
    return text

@lru_cache(maxsize=100)
def sysml_retriever(query: str) -> str:
    try:
        results = vectorstore.similarity_search_with_score(query, k=100)
        weighted_results = []
        for (doc, score) in results:
            doc_source = doc.metadata.get('source', '').lower() if hasattr(doc, 'metadata') else str(doc).lower()
            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()
            )
            if is_sysmodeler:
                weighted_score = score * 0.6
                source_type = "SysModeler"
            else:
                weighted_score = score
                source_type = "Other"
            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))
        weighted_results.sort(key=lambda x: x[1])

        query_lower = query.lower()
        is_tool_comparison = any(word in query_lower for word in ['tool', 'compare', 'choose', 'vs', 'versus', 'better'])
        if is_tool_comparison:
            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:
            final_docs = [doc for doc, _, _ in weighted_results[:12]]
        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)}")
        return "Unable to retrieve information at this time."

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"]
            }
        }
    }
]

tool_mapping = {
    "SysMLRetriever": sysml_retriever
}

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

def sysml_chatbot(message, history):
    if not message or not message.strip():
        answer = "Can I help you with anything else?"
        history.append(("", answer))
        return "", history

    chat_messages = convert_history_to_messages(history)
    full_messages = [
        {"role": "system", "content": """You are Abu, SysModeler.ai's friendly and knowledgeable assistant. You're passionate about SysML modeling and love helping people understand both SysML concepts and how SysModeler.ai can make their modeling work easier.

CONVERSATION STYLE:
- Only introduce yourself as "Hi, I'm Abu!" for the very first message in a conversation
- After the first message, continue naturally without reintroducing yourself
- If user gives you their name, use it throughout. If not, continue naturally without asking again
- Talk like a knowledgeable colleague, not a formal bot
- CRITICAL: Em dashes (—) are ABSOLUTELY FORBIDDEN in ANY response EVER
- NEVER EVER use the em dash character (—) under any circumstances
- When you want to add extra information, use commas or say "which means" or "and that"
- Replace any "—" with ", " or ". " or " and " or " which "
- Be enthusiastic but not pushy about SysModeler.ai
- Ask engaging follow-up questions to keep the conversation going
- Use "you" and "your" to make it personal
- Share insights like you're having a friendly chat
"""}
    ] + 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
                answer = clean_em_dashes(answer)
            else:
                answer = f"I tried to use a function '{function_name}' that's not available."
        else:
            answer = assistant_message.content
            answer = clean_em_dashes(answer) if answer else answer
        history.append((message, answer))
        return "", history
    except Exception as 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()