Update app.py

app.py

@@ -1,32 +1,59 @@
 import gradio as gr
 from transformers import pipeline
 
-# Function for image classification
 def classify(image, model_name):
     try:
-        # Load the pipeline with the given model name
         pipe = pipeline("image-classification", model=model_name)
-        # Perform image classification
         results = pipe(image)
         return {result["label"]: round(result["score"], 2) for result in results}
     except Exception as e:
-        # Handle errors gracefully, e.g., invalid model names
         return {"Error": str(e)}
 
-# Gradio Interface
-demo = gr.Interface(
-    fn=classify,
-    inputs=[
-        gr.Image(type="pil", label="Upload an Image"),
-        gr.Textbox(label="Enter timm Model Name", placeholder="e.g., timm/mobilenetv3_large_100.ra_in1k"),
-    ],
-    outputs=gr.Label(num_top_classes=3, label="Top Predictions"),
-    title="Custom timm Model Image Classifier",
-    description="Enter a timm model name from Hugging Face, upload an image, and get predictions.",
-    examples=[
-        ["cat.jpg", "timm/mobilenetv3_small_100.lamb_in1k"],
-        ["cat.jpg", "timm/resnet50.a1_in1k"],
-    ],
-)
+# Gradio Blocks Interface
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # Custom timm Model Image Classifier 🚀
+        
+        Explore the power of [timm](https://github.com/rwightman/pytorch-image-models) models for image classification using 
+        the Hugging Face [Transformers pipeline](https://huggingface.co/docs/transformers/main_classes/pipelines).
+        
+        With just a few lines of code, you can load any timm model hosted on the Hugging Face Hub and classify images effortlessly. 
+        This application demonstrates how you can use the pipeline API to create a powerful yet minimalistic image classification tool.
+        
+        ## How to Use
+        
+        1. Upload an image or use one of the provided examples.
+        2. Enter a valid timm model name from the Hugging Face Hub (e.g., `timm/resnet50.a1_in1k`).
+        3. View the top predictions and confidence scores!
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload an Image")
+            model_name_input = gr.Textbox(
+                label="Enter timm Model Name",
+                placeholder="e.g., timm/mobilenetv3_large_100.ra_in1k"
+            )
+        with gr.Column():
+            output_label = gr.Label(num_top_classes=3, label="Top Predictions")
 
+    gr.Examples(
+        examples=[
+            ["cat.jpg", "timm/mobilenetv3_small_100.lamb_in1k"],
+            ["cat.jpg", "timm/resnet50.a1_in1k"],
+        ],
+        inputs=[image_input, model_name_input]
+    )
+    submit_button = gr.Button("Classify")
+    submit_button.click(fn=classify, inputs=[image_input, model_name_input], outputs=output_label)
+
+    gr.Markdown(
+        """
+        ## Learn More
+        - Check out the implementation in the `app.py` file to see how easy it is to integrate timm models.
+        - Dive into the [official blog post on timm integration](https://huggingface.co/blog/timm-transformers) for more insights.
+        """
+    )
 demo.launch()