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| import gradio as gr | |
| from main import index, run | |
| from gtts import gTTS | |
| import os, time | |
| from transformers import pipeline | |
| p = pipeline("automatic-speech-recognition") | |
| """Use text to call chat method from main.py""" | |
| models = ["GPT-3.5", "Flan UL2", "Flan T5"] | |
| 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) | |
| 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) | |
| return history | |
| def get_first_message(history): | |
| history = [(None, | |
| 'Learn about <a href="https://www.coursera.org/learn/3d-printing-revolution/home">3D printing Revolution</a> course with referred sources. Try out the new voice to voice Q&A on the course! ')] | |
| return history | |
| def bot(history): | |
| return history | |
| with gr.Blocks() as demo: | |
| chatbot = gr.Chatbot(get_first_message([]), elem_id="chatbot").style(height=600) | |
| with gr.Row(): | |
| # Create radio button to select model | |
| radio = gr.Radio(models, label="Choose a model", value="GPT-3.5", type="value") | |
| with gr.Row(): | |
| with gr.Column(scale=0.75): | |
| txt = gr.Textbox( | |
| label="Coursera Voice Q&A Bot", | |
| placeholder="Enter text and press enter, or upload an image", lines=1 | |
| ).style(container=False) | |
| with gr.Column(scale=0.25): | |
| audio = gr.Audio(source="microphone", type="filepath").style(container=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]).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) | |