app.py

import os
import openai
from dotenv import load_dotenv
_ = load_dotenv()  # read local .env file

import gradio as gr
from langchain_chroma import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain_openai import OpenAIEmbeddings, ChatOpenAI

# Custom class to handle API routing for different models
class ChatOpenRouter(ChatOpenAI):
    openai_api_base: str
    openai_api_key: str
    model_name: str

    def __init__(self,
                 model_name: str,
                 openai_api_key: str = None,
                 openai_api_base: str = "https://openrouter.ai/api/v1",
                 **kwargs):
        openai_api_key = openai_api_key or os.getenv('OPENROUTER_API_KEY')
        super().__init__(openai_api_base=openai_api_base,
                         openai_api_key=openai_api_key,
                         model_name=model_name, **kwargs)

# Initialize embedding function here
embedding_function = OpenAIEmbeddings()

# Updated cbfs class with dynamic database and model selection
class cbfs:
    def __init__(self, persist_directory, model_name):
        self.chat_history = []
        self.answer = ""
        self.db_query = ""
        self.db_response = []
        self.panels = []
        # Initialize Chroma and the ConversationalRetrievalChain with the chosen database and model
        db = Chroma(persist_directory=persist_directory, embedding_function=embedding_function)
        retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3})
        
        # Select model dynamically
        if model_name == "GPT-4":
            chosen_llm = ChatOpenAI(model_name="gpt-4-1106-preview", temperature=0)
        # elif model_name == "GPT-3.5":
        #     chosen_llm = ChatOpenAI(model_name="gpt-3.5-turbo-0125", temperature=0)
        # elif model_name == "Llama-3 8B":
        #     chosen_llm = ChatOpenRouter(model_name="meta-llama/llama-3-8b-instruct", temperature=0)
        # elif model_name == "Gemini-1.5 Pro":
        #     chosen_llm = ChatOpenRouter(model_name="google/gemini-pro-1.5", temperature=0)
        # elif model_name == "Claude 3 Sonnet":
        #     chosen_llm = ChatOpenRouter(model_name='anthropic/claude-3-sonnet', temperature=0)
        # elif model_name == "Claude 3.5 Sonnet":
        #     chosen_llm = ChatOpenRouter(model_name='anthropic/claude-3.5-sonnet', temperature=0)
        else:
            # Default model
            # chosen_llm = ChatOpenRouter(model_name="meta-llama/llama-3-70b-instruct", temperature=0)
            chosen_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)

        self.qa = ConversationalRetrievalChain.from_llm(
            llm=chosen_llm,
            retriever=retriever,
            return_source_documents=True,
            return_generated_question=True,
        )

    def convchain(self, query):
        if not query:
            return [("User", ""), ("ChatBot", "")]
        result = self.qa.invoke({"question": query, "chat_history": self.chat_history})
        self.chat_history.append((query, result["answer"]))
        self.db_query = result["generated_question"]
        self.db_response = result["source_documents"]
        self.answer = result['answer']
        self.panels.append(["User", query])  # Ensure this is a list of two strings
        self.panels.append(["ChatBot", self.answer])  # Ensure this is a list of two strings
        return self.panels

    def clr_history(self):
        self.chat_history = []
        self.panels = []
        return self.panels  # Clear the chatbot display

# Create Gradio interface functions
def initialize_cbfs(db_choice, model_choice):
    """Initialize cbfs object based on the database and model selection and clear history."""
    if db_choice == "Covenants":
        return cbfs(persist_directory='docs/doc_cov/', model_name=model_choice)
    elif db_choice == "Bylaws":
        return cbfs(persist_directory='docs/doc_byl/', model_name=model_choice)
    else:
        return None

def chat_history(query, db_choice, model_choice, cb):
    """Handles chat submissions. Reminds the user to select a document if none is selected."""
    # cb = initialize_cbfs(db_choice, model_choice)  # Reinitialize cbfs 
    if cb is None:  # If cb is not initialized, remind to select a document
        return [("ChatBot", "Please select a document from the dropdown menu before submitting your query.")], ""
    else:
        return cb.convchain(query), ""  # Clear input box by returning empty string

def clear_history(cb):
    # cb = initialize_cbfs(db_choice, model_choice)  # Reinitialize cbfs to clear history
    if cb is None:  # Check if cbfs instance is None
        return [], ""  # No error message, simply clear the UI components
    else:
        cb.clr_history()
        return [], ""

# Create Gradio UI layout
with gr.Blocks() as demo:
    # Full-width image at the top
    with gr.Row():
        gr.Image("prloa.jpg", elem_id="full_width_image", show_label=False)

    # Full-width text below the image
    with gr.Row():
        gr.Markdown("<h1 style='text-align: center; font-size: 3.5em;'>Painted Rocks Lot-owners Association</h1>")
        
    gr.Markdown("# PRLOA Covenants and Bylaws ChatBot")

    with gr.Row():
        db_choice = gr.Dropdown(["Covenants", "Bylaws"], label="Select Document", scale=1)
        # model_choice = gr.Dropdown(["GPT-3.5", "GPT-4", "Llama-3 70B", "Llama-3 8B", "Gemini-1.5 Pro", "Claude 3 Sonnet", "Claude 3.5 Sonnet"], 
        #                            label="Select Model", scale=1, value = "Llama-3 70B")
        model_choice = gr.Dropdown(["GPT-3.5", "GPT-4"], 
                                   label="Select Model", scale=1, value = "GPT-3.5")
        button_clearhistory = gr.Button("Clear History", scale=1)

    with gr.Row():
        inp = gr.Textbox(placeholder="Enter text here…", scale=8)
        button_submit = gr.Button("Submit", scale=1)

    output = gr.Chatbot()

    # Initialize cbfs instance
    cbfs_instance = gr.State(initialize_cbfs(db_choice.value, model_choice.value))

    # Update cbfs_instance and clear chat history when the dropdown values change
    def update_cbfs_and_clear_history(db_choice, model_choice):
        new_cbfs = initialize_cbfs(db_choice, model_choice)
        if new_cbfs:
            new_cbfs.clr_history()
        return new_cbfs, [], ""  # Clear the chatbot display and input box

    db_choice.change(
        fn=update_cbfs_and_clear_history,
        inputs=[db_choice, model_choice],
        outputs=[cbfs_instance, output, inp]
    )

    model_choice.change(
        fn=update_cbfs_and_clear_history,
        inputs=[db_choice, model_choice],
        outputs=[cbfs_instance, output, inp]
    )

    # Define interactions for both submit button and Enter key
    inp.submit(fn=chat_history, inputs=[inp, db_choice, model_choice, cbfs_instance], outputs=[output, inp])
    button_submit.click(fn=chat_history, inputs=[inp, db_choice, model_choice, cbfs_instance], outputs=[output, inp])
    button_clearhistory.click(fn=clear_history, inputs=cbfs_instance, outputs=[output, inp])



# Launch the Gradio app
demo.launch()