ailib.py

import openai
import time

OPENAI_TRANSCRIPTION_MODEL='whisper-1'
OPENAI_COMPLETION_MODEL = "gpt-4o-mini"
TOKEN_LIMIT = 4096 

openai_client = openai.OpenAI()

def openai_query(prompt):
    response = openai_client.chat.completions.create(
            model = OPENAI_COMPLETION_MODEL,
            messages = [{'role': 'user',
                         'content': prompt}
                       ],
            temperature = 0, max_tokens = TOKEN_LIMIT,
            top_p=1.0, frequency_penalty=0.0, 
            presence_penalty=0.0)
    resp_text = response.choices[0].message.content      
    return resp_text

def nueva_ficha(resumen_historial, resumen_hoy):
    
    new_prompt = f'''Escribe una nueva ficha médica, considerando el historial médico: "{resumen_historial}",
                    y también lo que ocurrió hoy: "{resumen_hoy}"
                    '''
    nueva = openai_query(new_prompt)
    
    return nueva
    

def whisper_transcribe(fn, temperature=0):
    print('WT:', type(fn))
    t0 = time.time()
    
    if isinstance(fn, str):
        audio_file = open(fn,'rb')  # redundant?
    else:
        open('temp.wav','wb').write(fn)
        audio_file = 'temp.wav'
        
    whisper = openai_client.audio.transcriptions.create
    transcript = whisper(model=OPENAI_TRANSCRIPTION_MODEL,
                         file = audio_file, language='es',
                         temperature=0.0)        # to do, explore temperature
    dt = round(time.time()-t0,2)

    transcript = transcript.text
    print(f'Whisper transcribe [dt={dt} secs]')
    return transcript

def summarize(text):
    prompt = f"Resume todos los aspectos médicos de esta consulta: '{text}'"
    resp_text = openai_query(prompt)
    return resp_text