'''Imports'''
import tensorflow as tf
#import requests
from transformers import pipeline
import gradio as gr

'''Config inception'''
inception_net = tf.keras.applications.MobileNetV2()

'''Making request and set database'''
#response = requests.get("https://git.io/JJkYN")
#tags = response.text.split("\n")

'''Define model and classify pipelines'''
trans = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish")
classify = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis")

'''Define functions for demo'''
def classify_image(inp):
  inp = inp.reshape((-1, 224, 224, 3))
  inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
  prediction = inception_net.predict(inp).reshape(1,1000)
  pred_scores = tf.keras.applications.mobilenet_v2.decode_predictions(prediction, top=100)
  #prediction = inception_net.predict(inp).flatten()
  confidences = {f'{pred_scores[0][i][1]}': float(pred_scores[0][i][2]) for i in range(100)}
  #confidences = {tags[i]: float(prediction[i]) for i in range(1000)}
  return confidences

def audio_to_text(audio):
  text = trans(audio)["text"]
  return text

def text_to_sentiment(text):
  return classify(text)[0]["label"]

'''Define blocks for demo'''
demo = gr.Blocks()

'''Making demo'''
with demo:
  gr.Markdown("Second Demo with Blocks")
  with gr.Tabs():
    with gr.TabItem("Transcript audio in spanish"):
      with gr.Row():
        audio = gr.Audio(source="microphone", type="filepath")
        transcription = gr.Textbox()
      button1 = gr.Button("Please transcript")

    with gr.TabItem("Sentiment analisys"):
      with gr.Row():
        text = gr.Textbox()
        label = gr.Label()
      button2 = gr.Button("Please sentiment")

    with gr.TabItem("Image classify"):
      with gr.Row():
        image = gr.Image(shape=(224,224))
        labelImage = gr.Label(num_top_classes=3)
      button3 = gr.Button("Please classify Image")
    
    button1.click(audio_to_text, inputs=audio, outputs=transcription)
    button2.click(text_to_sentiment, inputs=text, outputs=label)
    button3.click(classify_image, inputs=image, outputs=labelImage)

demo.launch()