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Upload ailib.py
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ailib.py
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import openai
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import time
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OPENAI_TRANSCRIPTION_MODEL='whisper-1'
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OPENAI_COMPLETION_MODEL = "gpt-4o-mini"
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TOKEN_LIMIT = 4096
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openai_client = openai.OpenAI()
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def openai_query(prompt):
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response = openai_client.chat.completions.create(
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model = OPENAI_COMPLETION_MODEL,
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messages = [{'role': 'user',
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'content': prompt}
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],
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temperature = 0, max_tokens = TOKEN_LIMIT,
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top_p=1.0, frequency_penalty=0.0,
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presence_penalty=0.0)
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resp_text = response.choices[0].message.content
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return resp_text
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def nueva_ficha(resumen_historial, resumen_hoy):
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new_prompt = f'''Escribe una nueva ficha médica, considerando el historial médico: "{resumen_historial}",
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y también lo que ocurrió hoy: "{resumen_hoy}"
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'''
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nueva = openai_query(new_prompt)
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return nueva
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def whisper_transcribe(fn, temperature=0):
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print('WT:', type(fn))
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t0 = time.time()
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if isinstance(fn, str):
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audio_file = open(fn,'rb') # redundant?
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else:
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open('temp.wav','wb').write(fn)
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audio_file = 'temp.wav'
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whisper = openai_client.audio.transcriptions.create
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transcript = whisper(model=OPENAI_TRANSCRIPTION_MODEL,
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file = audio_file, language='es',
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temperature=0.0) # to do, explore temperature
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dt = round(time.time()-t0,2)
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transcript = transcript.text
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print(f'Whisper transcribe [dt={dt} secs]')
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return transcript
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def summarize(text):
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prompt = f"Resume todos los aspectos médicos de esta consulta: '{text}'"
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resp_text = openai_query(prompt)
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return resp_text
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