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import whisper
import gradio as gr
import openai
import os
openai.api_key = os.environ["OPENAI_API_KEY"]
model = whisper.load_model("small")
def transcribe(audio):
#time.sleep(3)
# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio(audio)
audio = whisper.pad_or_trim(audio)
# make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(audio).to(model.device)
# detect the spoken language
_, probs = model.detect_language(mel)
print(f"Detected language: {max(probs, key=probs.get)}")
# decode the audio
options = whisper.DecodingOptions(fp16 = False)
result = whisper.decode(model, mel, options)
return result.text
def process_text(input_text):
# Apply your function here to process the input text
output_text = input_text.upper()
return output_text
def get_completion(prompt, model='gpt-3.5-turbo'):
messages = [
{"role": "system", "content": """You are a world class nurse practitioner. You are provided with the transcription. \
Summarize the text and put it in a table format with rows as follows: \
Date of Alert
Claimant
Client/Employer
Claim #
DOI (Date of Injury)
Date of Visit
Provider
Diagnosis Treated
Subjective findings
Objective Findings
Treatment plan
Medications
RTW (Return to Work) Status
Restrictions
NOV (Next Office Visit)
"""
},
{"role": "user", "content": prompt}
]
response = openai.ChatCompletion.create(
model = model,
messages = messages,
temperature = 0,
)
return response.choices[0].message['content']
demo = gr.Blocks()
with demo:
audio = gr.Audio(source="microphone", type="filepath")
b1 = gr.Button("Transcribe audio")
b2 = gr.Button("Process text")
text1 = gr.Textbox()
text2 = gr.Textbox()
prompt = text1
b1.click(transcribe, inputs=audio, outputs=text1)
b2.click(get_completion, inputs=text1, outputs=text2)
# b1.click(transcribe, inputs=audio, outputs=text1)
# b2.click(get_completion, inputs=prompt, outputs=text2)
demo.launch()
# In this example, the process_text function just converts the input text to uppercase, but you can replace it with your desired function. The Gradio Blocks interface will have two buttons: "Transcribe audio" and "Process text". The first button transcribes the audio and fills the first textbox, and the second button processes the text from the first textbox and fills the second textbox.
# gr.Interface(
# title = 'OpenAI Whisper ASR Gradio Web UI',
# fn=transcribe,
# inputs=[
# gr.inputs.Audio(source="microphone", type="filepath")
# ],
# outputs=[
# "textbox"
# ],
# live=True).launch()
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