app.py

import time
import uuid

import gradio as gr
from gtts import gTTS
from transformers import pipeline

from main import index, run

p = pipeline("automatic-speech-recognition", model="openai/whisper-base")

"""Use text to call chat method from main.py"""

models = ["GPT-3.5", "Flan UL2", "Flan T5"]

with gr.Blocks(theme='snehilsanyal/scikit-learn') as demo:
    state = gr.State([])


    def create_session_id():
        return str(uuid.uuid4())


    def add_text(history, text, model):
        print("Question asked: " + text)
        response = run_model(text, model)
        history = history + [(text, response)]
        print(history)
        return history, ""


    def run_model(text, model):
        start_time = time.time()
        print("start time:" + str(start_time))
        response = run(text, model, state.session_id)
        end_time = time.time()
        # If response contains string `SOURCES:`, then add a \n before `SOURCES`
        if "SOURCES:" in response:
            response = response.replace("SOURCES:", "\nSOURCES:")
            # response = response + "\n\n" + "Time taken: " + str(end_time - start_time)
        print(response)
        print("Time taken: " + str(end_time - start_time))
        return response


    def get_output(history, audio, model):
        txt = p(audio)["text"]
        # history.append(( (audio, ) , txt))
        audio_path = 'response.wav'
        response = run_model(txt, model)
        # Remove all text from SOURCES: to the end of the string
        trimmed_response = response.split("SOURCES:")[0]
        myobj = gTTS(text=trimmed_response, lang='en', slow=False)
        myobj.save(audio_path)
        # split audio by / and keep the last element
        # audio = audio.split("/")[-1]
        # audio = audio + ".wav"
        history.append(((audio,), (audio_path,)))
        print(history)
        return history


    def set_model(history, model):
        print("Model selected: " + model)
        history = get_first_message(history)
        index(model, state.session_id)
        return history


    def get_first_message(history):
        history = [(None,
                    'Learn about the course and get answers with sources.\n This is an experiment using AI, so it might make errors')]
        return history


    def bot(history):
        return history


    state.session_id = create_session_id()
    print("Session ID: " + state.session_id)
    # Title on top in middle of the page
    # gr.HTML("<h1 style='text-align: center;'>Course Assistant - 3D Printing Revolution</h1>")

    chatbot = gr.Chatbot(get_first_message([]), elem_id="chatbot", label='3D Printing Applications Question Answer Bot').style(height=300,
                                                                                                         container=False)

    # with gr.Row():
    # Create radio button to select model
    radio = gr.Radio(models, label="Choose a model", value="GPT-3.5", type="value", visible=False)
    with gr.Row():
        # with gr.Column(scale=0.75):
        txt = gr.Textbox(
            label="Ask your question here and press enter",
            placeholder="Enter text and press enter", lines=1
        ).style(container=False)

        # with gr.Column(scale=0.25):
        audio = gr.Audio(source="microphone", type="filepath", visible=False)

    txt.submit(add_text, [chatbot, txt, radio], [chatbot, txt], postprocess=False).then(
        bot, chatbot, chatbot
    )

    audio.change(fn=get_output, inputs=[chatbot, audio, radio], outputs=[chatbot], show_progress=True).then(
        bot, chatbot, chatbot
    )

    radio.change(fn=set_model, inputs=[chatbot, radio], outputs=[chatbot]).then(bot, chatbot, chatbot)

    audio.change(lambda: None, None, audio)

    set_model(chatbot, radio.value)

if __name__ == "__main__":
    demo.queue()
    demo.queue(concurrency_count=5)
    demo.launch(debug=True)