restore working version

app.py

@@ -37,39 +37,6 @@ search_terms_wikipedia = {
     "goldfields coreopsis": "https://en.wikipedia.org/wiki/Coreopsis"
 }
 
-flowers_endangerment = {
-    "Blazing Star": "Not considered endangered.",
-    "Bristlecone Pine": "Least Concern (stable population).",
-    "California Bluebell": "Not listed as endangered or threatened.",
-    "California Buckeye": "Not endangered.",
-    "California Buckwheat": "Generally secure.",
-    "California Fuchsia": "Not endangered overall; some subspecies at risk.",
-    "California Checkerbloom": "Not generally endangered; some subspecies critically imperiled.",
-    "California Lilac": "Most species not endangered; some species are endangered.",
-    "California Poppy": "Generally secure; some subspecies face threats.",
-    "California Sagebrush": "Considered secure (G4-G5).",
-    "California Wild Grape": "Apparently secure (G4).",
-    "California Wild Rose": "Secure (G4).",
-    "Coyote Mint": "Varies by species; some federally listed as endangered.",
-    "Elegant Clarkia": "Secure (G5).",
-    "Baby Blue Eyes": "Secure.",
-    "Hummingbird Sage": "Apparently secure (G4).",
-    "Delphinium": "Varies by species; some are endangered.",
-    "Matilija Poppy": "Not currently endangered.",
-    "Blue-Eyed Grass": "Not endangered.",
-    "Penstemon Spectabilis": "Not endangered.",
-    "Seaside Daisy": "Not endangered.",
-    "Sticky Monkeyflower": "Not endangered.",
-    "Tidy Tips": "Generally not endangered; some subspecies may be at risk.",
-    "Wild Cucumber": "Generally not endangered.",
-    "Douglas Iris": "Not endangered.",
-    "Goldfields Coreopsis": "Varies by species; many not endangered."
-}
-
-def get_status(flower_name):
-    """Return the endangerment status of a given flower name."""
-    return flowers_endangerment.get(flower_name, "Flower not found in database.")
-
 # Templates for AI image generation
 prompt_templates = [
     "A dreamy watercolor scene of a {flower} on a misty morning trail, with golden sunbeams filtering through towering redwoods, and a curious hummingbird hovering nearby.",
@@ -95,34 +62,16 @@ def on_queue_update(update):
         for log in update.logs:
            print(log["message"])
 
-
-
-def get_status(flower_name):
-    """Return the endangerment status of a given flower name."""
-    # Normalize input for dictionary lookup
-    normalized_name = flower_name.title()
-    return flowers_endangerment.get(normalized_name, "Flower not found in database.")
-
-
-
-
 # Main function to process the uploaded image
 def process_image(img):
-    print("Starting prediction...")
+    # Classify the image
     predicted_class, _, probs = learn.predict(img)
-    print(f"Prediction complete: {predicted_class}")
-    
     classification_results = dict(zip(learn.dls.vocab, map(float, probs)))
     
     # Get Wikipedia link
     wiki_url = search_terms_wikipedia.get(predicted_class, "No Wikipedia entry found.")
     
-    # Get endangerment status
-    endangerment_status = get_status(predicted_class)
-    print(f"Status found: {endangerment_status}")
-    
-    # Generate artistic interpretation
-    print("Sending request to FAL API...")
+    # Generate artistic interpretation by calling the Flux API
     result = fal_client.subscribe(
         "fal-ai/flux/schnell",
         arguments={
@@ -132,29 +81,22 @@ def process_image(img):
         with_logs=True,
         on_queue_update=on_queue_update,
     )
-    print("FAL API responded")
-
-    # Retrieve image
+    
+    # Get the generated image
     image_url = result['images'][0]['url']
-    print(f"Image URL: {image_url}")
     response = requests.get(image_url)
     generated_image = Image.open(io.BytesIO(response.content))
     
-    print("Image retrieved and ready to return")
-    return classification_results, #generated_image, wiki_url, endangerment_status
-
-
+    return classification_results, generated_image, wiki_url
 
 # Function to clear all outputs
 def clear_outputs():
     return {
         label_output: None,
-       # generated_image: None,
-       # wiki_output: None,
-        #status_output: None  # ← NEW
+        generated_image: None,
+        wiki_output: None
     }
 
-
 # Load the AI model
 learn = load_learner('resnet50_30_categories.pkl')
 
@@ -165,34 +107,33 @@ with gr.Blocks() as demo:
         input_image = gr.Image(height=230, width=230, label="Upload Image for Classification", type="pil")
     
     # Output section
-   # 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)
-            #status_output = gr.Textbox(label="Endangerment Status", lines=1)  # ← NEW
-        #generated_image = gr.Image(label="AI Generated Interpretation")
-
+            wiki_output = gr.Textbox(label="Wikipedia Article Link", lines=1)
+        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
-)
-
-
+        examples_per_page=6,
+        fn=process_image,
+        outputs=[label_output, generated_image, wiki_output]
+    )
+    
+    # Set up what happens when an image is uploaded or removed
     input_image.change(
         fn=process_image,
         inputs=input_image,
-        outputs=[label_output]#, generated_image, wiki_output, status_output]  # ← UPDATED
-)
-
+        outputs=[label_output, generated_image, wiki_output]
+    )
+    
     input_image.clear(
         fn=clear_outputs,
         inputs=[],
-        outputs=[label_output]#, generated_image, wiki_output, status_output]  # ← UPDATED
-)
-
+        outputs=[label_output, generated_image, wiki_output]
+    )
 
 # Start the application
 demo.launch(inline=False)