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| from openai import OpenAI | |
| import gradio as gr | |
| from transformers import pipeline | |
| import numpy as np | |
| transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en") | |
| qa_model = pipeline("question-answering", model="distilbert-base-cased-distilled-squad") | |
| def predict(message, history, api_key): | |
| print('in predict') | |
| client = OpenAI(api_key=api_key) | |
| history_openai_format = [] | |
| if 0: | |
| for human, assistant in history: | |
| history_openai_format.append({"role": "user", "content": human}) | |
| history_openai_format.append({"role": "assistant", "content": assistant}) | |
| history_openai_format.append({"role": "user", "content": message}) | |
| response = client.chat.completions.create( | |
| model='gpt-4o', | |
| messages=history_openai_format, | |
| temperature=1.0, | |
| stream=True | |
| ) | |
| partial_message = "" | |
| for chunk in response: | |
| if chunk.choices[0].delta.content: | |
| print(111, chunk.choices[0].delta.content) | |
| partial_message += chunk.choices[0].delta.content | |
| yield partial_message | |
| def chat_with_api_key(api_key, message, history): | |
| print('in chat_with_api_key') | |
| accumulated_message = "" | |
| for partial_message in predict(message, history, api_key): | |
| accumulated_message = partial_message | |
| history.append((message, accumulated_message)) | |
| yield accumulated_message, history | |
| def transcribe(audio): | |
| if audio is None: | |
| return "No audio recorded." | |
| sr, y = audio | |
| y = y.astype(np.float32) | |
| y /= np.max(np.abs(y)) | |
| return transcriber({"sampling_rate": sr, "raw": y})["text"] | |
| def clear_all(): | |
| return None, "", "" | |
| with gr.Blocks() as demo: | |
| #output = gr.Textbox(label="Output", elem_id="output") | |
| chatbot = gr.Chatbot(label="Chat Output") | |
| with gr.Row(): | |
| api_key = gr.Textbox(label="API Key", placeholder="Enter your API key", type="password") | |
| message = gr.Textbox(label="Message") | |
| gr.Markdown("# Audio Transcription and Question Answering") | |
| with gr.Row(): | |
| audio_input = gr.Audio(label="Audio Input", sources=["microphone"], type="numpy") | |
| with gr.Column(): | |
| transcription_output = gr.Textbox(label="Transcription") | |
| clear_button = gr.Button("Clear") | |
| state = gr.State([]) | |
| def update_output(api_key, audio_input, state): | |
| print('in update_output') | |
| message = transcribe(audio_input) | |
| responses = chat_with_api_key(api_key, message, state) | |
| accumulated_response = "" | |
| for response, updated_state in responses: | |
| accumulated_response = response | |
| yield accumulated_response, updated_state | |
| btn = gr.Button("Submit") | |
| btn.click(update_output, inputs=[api_key, message, state], outputs=[chatbot, state]) | |
| audio_input.stop_recording( | |
| fn=update_output, | |
| inputs=[api_key, audio_input, state], | |
| outputs=[chatbot, state] | |
| ) | |
| clear_button.click( | |
| fn=clear_all, | |
| inputs=[], | |
| outputs=[audio_input, transcription_output, chatbot] | |
| ) | |
| gr.HTML(""" | |
| <style> | |
| #output { | |
| overflow-y: auto; | |
| max-height: 800px; | |
| }""") | |
| demo.launch() | |