Update app.py

app.py

@@ -1,33 +1,56 @@
 import gradio as gr
 from transformers import pipeline
+from PIL import Image
 
 trans=pipeline("automatic-speech-recognition",model="facebook/wav2vec2-large-xlsr-53-spanish")
-clasificador=pipeline("text-classification",model="pysentimiento/robertuito-sentiment-analysis")
+sentiment_cla=pipeline("text-classification",model="pysentimiento/robertuito-sentiment-analysis")
+image_cla=pipeline("image-classification", model="microsoft/swin-tiny-patch4-window7-224")
 
 def audio2text(audio):
   text = trans(audio)["text"]
   return text
 
 def text2sentiment(text):
-  return clasificador(text)[0]["label"]
+  return sentiment_cla(text)[0]["label"]
 
-demo=gr.Blocks()
-with demo:
-  gr.Markdown("Gradio + Blocks + TabItem")
+def classify_img(im):
+    im = Image.fromarray(im.astype('uint8'), 'RGB')
+    ans = image_cla(im)
+    labels = {v["label"]: v["score"] for v in ans}
+    return labels
+
+def make_block(dem):
+    with dem:
+        gr.Markdown("""
+# Este 'space' permite la inferencia los siguientes modelos open-source:
+- Voice2Text: [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-large-xlsr-53-spanish)
+- Sentiment Analysis: [Robertuito](https://huggingface.co/pysentimiento/robertuito-sentiment-analysis)
+- Image Classifier: [Swin-small-patch4](https://huggingface.co/microsoft/swin-small-patch4-window7-224)
+Autor del demo: [ΜΕΤΑΝΘΡΩΠΙΑ](https://www.instagram.com/metantropia.jpg)
+        """)
+    
   with gr.Tabs():
-    with gr.TabItem("Transcribe audio en espanol"):
+      
+    with gr.TabItem("Audio a Texto"):
       with gr.Row():
         audio=gr.Audio(sources="microphone",type="filepath")
         transcripcion=gr.Textbox()
       b1=gr.Button("Transcribe")
 
-    with gr.TabItem("Analisis de sentimiento en espanol"):
+    with gr.TabItem("Analisis de Sentimiento"):
       with gr.Row():
         text=gr.Textbox()
         label=gr.Label()
       b2=gr.Button("Sentimiento")
 
+    with gr.TabItem("Clasificación de Imágenes"):
+      with gr.Row():
+        image = gr.Image(label="Carga una imagen aquí")
+        label_image = gr.Label(num_top_classes=5)
+      b3 = gr.Button("Clasifica")
+
       b1.click(audio2text,inputs=audio,outputs=transcripcion)
       b2.click(text2sentiment,inputs=text,outputs=label)
+      b3.click(classify_img, inputs=image, outputs=label_image)
 
 demo.launch()