com

app.py

@@ -1,18 +1,38 @@
 import gradio as gr
-from transformers import pipeline
+import os
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-def predict(input_img):
-    predictions = pipeline(input_img)
-    return input_img, {p["label"]: p["score"] for p in predictions} 
+def combine(a, b):
+    return a + " " + b
 
-gradio_app = gr.Interface(
-    predict,
-    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
-    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
-    title="Hot Dog? Or Not?",
-)
+
+def mirror(x):
+    return x
+
+
+with gr.Blocks() as demo:
+
+    txt = gr.Textbox(label="Input", lines=2)
+    txt_2 = gr.Textbox(label="Input 2")
+    txt_3 = gr.Textbox(value="", label="Output")
+    btn = gr.Button(value="Submit")
+    btn.click(combine, inputs=[txt, txt_2], outputs=[txt_3])
+
+    with gr.Row():
+        im = gr.Image()
+        im_2 = gr.Image()
+
+    btn = gr.Button(value="Mirror Image")
+    btn.click(mirror, inputs=[im], outputs=[im_2])
+
+    gr.Markdown("## Text Examples")
+    gr.Examples(
+        [["hi", "Adam"], ["hello", "Eve"]],
+        [txt, txt_2],
+        txt_3,
+        combine,
+        cache_examples=True,
+    )
 
 if __name__ == "__main__":
-    gradio_app.launch()
+    demo.launch()