Add application files

app.py

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+import gradio as gr
+from transformers import pipeline
+
+global pipeline
+global default_model_name
+default_model_name = "google/vit-base-patch16-224"
+
+def predict(image, model_name):
+    pipe = pipeline(task="image-classification", model=model_name)
+    predictions = pipe(image)
+    return {p["label"]: p["score"] for p in predictions}
+    
+    
+with gr.Blocks() as demo:
+    
+    with gr.Row():
+        gr.Markdown(
+        """
+        # Settings
+        [Here](https://huggingface.co/models?pipeline_tag=image-classification&sort=downloads) are some popular image classification models.  
+        Or use default model **"google/vit-base-patch16-224"**
+        """)
+        gr.Markdown(
+        """
+        # Image Classifier Result
+        """)
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_model = gr.Textbox(label="Enter a custom model name:", value=default_model_name, scale=1)
+           
+                
+            gr.Markdown("Upload image")
+            #images_input = gr.File(file_count="multiple", file_types=["image"], label="Input images", scale=1)
+            #images_input = gr.Files(file_count="multiple", file_types=["image"], label="Input images", scale=1)
+            input_image = gr.Image(label="Input Image", type="filepath")
+
+        #output = gr.Label(label="Output", num_top_classes=3, scale=2)
+        output = gr.Label(num_top_classes=10, scale=2)
+    with gr.Row(equal_height=True):
+        clear_button = gr.ClearButton(value="Clear", scale=0)
+        submit_button = gr.Button(value="Submit", variant="primary", scale=0)
+    submit_button.click(fn=predict, inputs=[input_image, input_model], outputs=output)
+    clear_button.click(lambda: [None, None, None], outputs=[input_model, input_image, output])
+    
+demo.launch()

requirements.txt

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+gradio==3.44.4
+transformers==4.33.2