import gradio as gr from main import index, run from gtts import gTTS import os from transformers import pipeline p = pipeline("automatic-speech-recognition") """Use text to call chat method from main.py""" def add_text(history, text): print("Question asked: " + text) response = run_model(text) history = history + [(text, response)] print(history) return history, "" def run_model(text): response = run(question=text) # If response contains string `SOURCES:`, then add a \n before `SOURCES` if "SOURCES:" in response: response = response.replace("SOURCES:", "\nSOURCES:") print(response) return response def fill_input_textbox(history, audio): txt = p(audio)["text"] response = run_model(txt) # 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("response.wav") audio = audio + ".wav" history = history + [((audio, ), ('response.wav', ))] print(history) return history def bot(history): return history with gr.Blocks() as demo: index() chatbot = gr.Chatbot([(None,'Learn about 3D printing Revolution course with referred sources. Try out the new voice to voice Q&A on the course! ')], elem_id="chatbot").style(height=750) with gr.Row(): with gr.Column(scale=0.85): 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=0.15): audio = gr.Audio(source="microphone", type="filepath").style(container=False) txt.submit(add_text, [chatbot, txt], [chatbot, txt], postprocess=False).then( bot, chatbot, chatbot ) audio.change(fn=fill_input_textbox,inputs=[chatbot, audio], outputs=[chatbot]).then( bot, chatbot, chatbot ) if __name__ == "__main__": demo.launch()