Update app.py

app.py

@@ -4,6 +4,8 @@ import random
 from fastai.vision.all import *
 from openai import OpenAI
 from pathlib import Path
+from PIL import Image
+import io
 
 # Dictionary of plant names and their Wikipedia links
 search_terms_wikipedia = {
@@ -122,7 +124,8 @@ def process_image(img, generate_image=True):
             )
 
             image_base64 = result.data[0].b64_json
-            generated_image = base64.b64decode(image_base64)
+            image_bytes = base64.b64decode(image_base64)
+            generated_image = Image.open(io.BytesIO(image_bytes))
         else:
             generated_image = None
 
@@ -148,20 +151,39 @@ def clear_outputs():
 learn = load_learner('resnet50_30_categories.pkl')
 
 # Create the web interface
-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."
-)
+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]
+    )
 
 # Start the application
 demo.launch(inline=False)