Update app.py

app.py

@@ -148,39 +148,20 @@ def clear_outputs():
 learn = load_learner('resnet50_30_categories.pkl')
 
 # Create the web interface
-with gr.Blocks() as demo:
-    # Input section
-    with gr.Row():
-        input_image = gr.Image(height=230, width=230, label="Upload Image for Classification", type="pil")
-
-    # Output section
-    with gr.Row():
-        with gr.Column():
-            label_output = gr.Label(label="Classification Results")
-            wiki_output = gr.Textbox(label="Wikipedia Article Link", lines=1)
-            endangerment_output = gr.Textbox(label="Endangerment Status", lines=1)  # ← NEW
-        generated_image = gr.Image(label="AI Generated Interpretation")
-
-    # Add example images using local paths
-    gr.Examples(
-        examples=example_images,
-        inputs=input_image,
-        examples_per_page=6,
-        fn=None,
-        outputs=None
-    )
-
-    input_image.change(
-        fn=lambda img: process_image(img, generate_image=True),
-        inputs=input_image,
-        outputs=[label_output, generated_image, wiki_output, endangerment_output]
-    )
-
-    input_image.clear(
-        fn=clear_outputs,
-        inputs=[],
-        outputs=[label_output, generated_image, wiki_output, endangerment_output]
-    )
+demo = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(height=230, width=230, label="Upload Image for Classification", type="pil"),
+    outputs=[
+        gr.Label(label="Classification Results"),
+        gr.Image(label="AI Generated Interpretation"),
+        gr.Textbox(label="Wikipedia Article Link", lines=1),
+        gr.Textbox(label="Endangerment Status", lines=1)
+    ],
+    examples=example_images,
+    examples_per_page=6,
+    title="California Native Flower Classifier",
+    description="Upload an image of a California native flower to classify it and see an AI-generated interpretation."
+)
 
 # Start the application
 demo.launch(inline=False)