simple model

app.py

@@ -1,66 +1,15 @@
 import gradio as gr
-import requests
-import tensorflow as tf
-from transformers import pipeline, AutoModelForCTC, AutoTokenizer
+from transformers import pipeline
 
-model_name = "facebook/wav2vec2-large-xlsr-53-spanish"
-model = AutoModelForCTC.from_pretrained(model_name, from_pt=True)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-trans = pipeline("automatic-speech-recognition", model=model, tokenizer=tokenizer)
+# audio2text
+trans = pipeline("automatic-speech-recognition", model = "facebook/wav2vec2-large-xlsr-53-spanish")
 
 def audio2text(audio):
   text = trans(audio)["text"]
   return text
 
-
-# text2sentiment
-classifier = pipeline("text-classification", model = "pysentimiento/robertuito-sentiment-analysis")
-
-def text2sentiment(text):
-  return classifier(text)[0]["label"]
-
-
-# image_classification
-inception_net = tf.keras.applications.MobileNetV2()
-
-answer = requests.get("https://git.io/JJkYN")
-labels = answer.text.split("\n")
-
-def image_classification(inp):
-  inp = inp.reshape((-1,224,224,3))
-  inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
-  prediction = inception_net.predict(inp).flatten()
-  confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
-  return confidences
-
-
-# demo
-demo = gr.Blocks()
-
-with demo:
-  gr.Markdown("This is the second demo with Blocks")
-  with gr.Tabs():
-    with gr.TabItem("Transcribe audio in Spanish"):
-      with gr.Row():
-        audio = gr.Audio(source="microphone", type="filepath")
-        transcription = gr.Textbox()
-      b1 = gr.Button("Transcribe")
-
-    with gr.TabItem("Sentiment analysis in Spanish"):
-      with gr.Row():
-        text = gr.Textbox()
-        label_sentiment = gr.Label()
-      b2 = gr.Button("Sentiment")
-
-    with gr.TabItem("Image classification"):
-      with gr.Row():
-        image=gr.Image(shape=(224,224))
-        label_image=gr.Label(num_top_classes=3)
-      b3 = gr.Button("Classify")
-
-    b1.click(audio2text, inputs = audio, outputs=transcription)
-    b2.click(text2sentiment, inputs=text, outputs=label_sentiment)
-    b3.click(image_classification, inputs=image, outputs=label_image)
-
-demo.launch()
+gr.Interface(
+    fn=transcribe,
+    inputs = [gr.Audio(source="microphone", type="filepath")],
+    outputs=["textbox"]
+).launch()