app.py

import gradio as gr
from pydub import AudioSegment
from ailib import whisper_transcribe, summarize, nueva_ficha
import pandas as pd

def transcribe(audiofile):
    #audio = AudioSegment.from_file(audiofile)
    #open(filename, 'wb').write(audio)
    consulta = whisper_transcribe(audiofile)
    resumen_consulta = summarize(consulta)
    resumen_historial = open('resumen.txt').read()
    nueva = nueva_ficha(resumen_historial, resumen_consulta)
    
    return consulta, resumen_consulta, nueva

def display_text(paciente):
    resumen_historial = df[df.paciente==paciente]['Resumen'].iloc[0]
    historial = df[df.paciente==paciente]['EHR'].iloc[0]    
    open('resumen.txt', 'w').write(resumen_historial)
    
    return resumen_historial, historial

df = pd.read_csv('pacientes.csv')
pacientes = list(df['paciente'].unique())             # now contains PAC_ID
print('PAX:', pacientes, type(pacientes))
pid_dict = dict(zip(df.paciente, df.pac_id))
################################################
gr.Markdown("**Demo CORFO**")

with gr.Blocks() as demo:
    with gr.Row():     
        audemo = gr.Interface(
            transcribe,
            gr.Audio(sources=["microphone"], type="filepath"),
            ["text","text","text"]   # was simply "text",
        )
    with gr.Row():
        with gr.Column(scale=1):
            radio = gr.Radio(choices=pacientes, 
                             label="PACIENTE", value=pacientes[0], interactive=True)
        with gr.Column(scale=4):
            with gr.Row():   # historial+resumen
                resumen_trans = gr.Markdown()
                texto_nueva_ficha = gr.Markdown()
                
            with gr.Row():   # historial+resumen
                with gr.Accordion('Resumen Historial'):   # use radio?
                    left_text = gr.Markdown()
                with gr.Accordion('Historial'):
                    right_text = gr.Markdown()
    
    radio.change(display_text, inputs=radio, 
                 outputs=[left_text, right_text])

demo.launch()