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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 answer(transcription):
context = "You are chatbot answering general questions"
print(transcription)
result = qa_model(question=transcription, context=context)
print(result)
return result['answer']
def clear_all():
return None, "", ""
with gr.Blocks() as demo:
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([])
output = gr.Textbox(label="Output", lines=10)
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=[output, state])
audio_input.stop_recording(
fn=update_output,
inputs=[api_key, audio_input, state],
outputs=[output, state]
)
clear_button.click(
fn=clear_all,
inputs=[],
outputs=[audio_input, transcription_output, output]
)
demo.launch()
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