app.py

import whisper
import gradio as gr
import openai 
import os

openai.api_key = os.environ["OPENAI_API_KEY"]

#openai.api_key = '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'

import whisper
import gradio as gr

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": "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 = f"""
    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: \ 
        
    1. Patient identification: 
    2. Chief complaint: 
    3. Medical history: 
    4. Family history: 
    5. Social history: 
    6. Review of systems: 
    7. Current medications: 
    8. Vaccination status: 
    9. Emotional well-being: 
    10. Patient concerns and expectations: 
    
  
    """
    
    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=text1, outputs=text2)



demo.launch(share=True)

# 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()