- Modified generate_answer() to be compatible with the ui (streaming). Also in...

        for chunk in self._embedder.search_similar(self._collection_name, question, n_results=self._n_results):

            prompt += chunk + "\n\n"

        print(prompt)

        print(prompt) # TODO Delete this line

        return prompt

    def _fetch_conversation(self) -> list[dict[str, str]]:

        return True if not self._conversation else False

    ## ====== CALLABLE METHODS ====== ##

    def generate_answer(self, question, model) -> None:

    def generate_answer(self, question, model):

        """

        Generates an answer to the given question using the given model.

        :param model: The model to use for generating the answer.

        :param question: The question to answer.

        :return: None

        :return: generator of strings. Each string represents a chunk of the answer.

        """

        # Set the model to the specified one

        self._model = model

        answer = ""

        for chunk in answering_fn():

            print(chunk, end="")

            yield chunk

            answer += chunk

        # Save only the user's question

        self._conversation[-1]["content"] = question

        # Save answer to the conversation

        self._update_conversation("assistant", answer)

        pass

embedder = Embedder()

embedder.load_docs(directory="aiani dedomena/*", chunking_type=Embedder.ByChar)

if not embedder.collection_exists("Mycollection"):

    embedder.add_data("Mycollection")

gen = Generator(embedder=embedder, collection_name="Mycollection", n_results=5)

while True:

    gen.generate_answer(input("Ask a question: "), model="gpt-4o-mini")

    print("\n")

# embedder = Embedder()

# embedder.load_docs(directory="aiani dedomena/*", chunking_type=Embedder.ByChar)

#

# if not embedder.collection_exists("Mycollection"):

#     embedder.add_data("Mycollection")

#

# gen = Generator(embedder=embedder, collection_name="Mycollection", n_results=5)

#

# while True:

#     print("==================================================")

#     gen.generate_answer(input("Ask a question: "), model="gpt-4o-mini")

#     print("\n")

#     print("==================================================")

#     print(f"conversation:\n{gen._fetch_conversation()}\n\n")

# embedder.visualize("Mycollection", dimensions=["2d", "3d"])

import gradio as gr  # Ensure the Gradio library is installed by running: pip install gradio

from generator import Generator

from embedder import Embedder

class UIController:

    def __init__(self):

        self.gen = Generator(Embedder(), "Mycollection", n_results=5)

        self.embedder = Embedder()

        self._prepare_embedder()

    # ===   PRIVATE METHODS   === #

    def _prepare_embedder(self):

        """

        Loads the documents and creates the collection.

        :return: None

        """

        if not self.embedder.collection_exists("Mycollection"):

            self.embedder.load_docs(directory="aiani dedomena/*", chunking_type=Embedder.ByChar)

            self.embedder.add_data("Mycollection")

    def _user(self, user_message, history: list):

        """

        Creates and updates the history of the conversation.

        :param user_message: The user's message to add to the history.

        :param history: The history of the conversation.

        :return: A tuple that consists of a blank string and the updated history.

        The blank string will be sent at the textbox and the history at the chatbot.

        """

        return "", history + [{"role": "user", "content": user_message}]

    def _bot(self, history: list):

        """

        Generates an answer to the user's question and updates the history.

        :param history: The previous history of the conversation.

        :return: A generator of strings. Each string represents a chunk of the answer.

        The answer will be sent at the chatbot.

        """

        print(history)

        question = history[-1]["content"]

        bot_message = self.gen.generate_answer(question, model="gpt-4o-mini")

        history.append({"role": "assistant", "content": ""})

        for chunk in bot_message:

            history[-1]['content'] += chunk

            yield history

    # ====  CALLABLE METHODS   === #

    def create_ui(self, share=False):

        """

        Creates the UI.

        :param share: If True, a public link will be generated for the UI, else the host will be localhost.

        :return: None.

        """

        with gr.Blocks() as demo:

            with gr.Row():

                gr.Markdown("AI Ξεναγός!")

            bot = gr.Chatbot(type="messages")

            msg = gr.Textbox()

            # submit = gr.Button("Submit")

            # submit.click(fn=self.gen.generate_answer, inputs=[input], outputs=[output]).then

            msg.submit(self._user, [msg, bot], [msg, bot], queue=False).then(

                self._bot, bot, bot

            )

        demo.launch(share=share)

ui = UIController()

ui.create_ui(share=True)

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