Update app.py

app.py

@@ -1,22 +1,31 @@
-!pip install transformers streamlit
+import numpy as np
+import gradio as gr
 
-import streamlit as st
-from transformers import pipeline
-from PIL import Image
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+def flip_text(x):
+    return x[::-1]
 
-st.title("Hot Dog? Or Not?")
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
+def flip_image(x):
+    return np.fliplr(x)
 
-if file_name is not None:
-    col1, col2 = st.columns(2)
 
-    image = Image.open(file_name)
-    col1.image(image, use_column_width=True)
-    predictions = pipeline(image)
+with gr.Blocks() as demo:
+    gr.Markdown("Flip text or image files using this demo.")
+    with gr.Tab("Flip Text"):
+        text_input = gr.Textbox()
+        text_output = gr.Textbox()
+        text_button = gr.Button("Flip")
+    with gr.Tab("Flip Image"):
+        with gr.Row():
+            image_input = gr.Image()
+            image_output = gr.Image()
+        image_button = gr.Button("Flip")
 
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+    with gr.Accordion("Open for More!"):
+        gr.Markdown("Look at me...")
+
+    text_button.click(flip_text, inputs=text_input, outputs=text_output)
+    image_button.click(flip_image, inputs=image_input, outputs=image_output)
+
+demo.launch()