Update app.py

app.py

@@ -37,7 +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).",
@@ -109,8 +108,10 @@ def get_status(flower_name):
 
 # Main function to process the uploaded image
 def process_image(img):
-    # Classify the image
+    print("Starting prediction...")
     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
@@ -118,8 +119,10 @@ def process_image(img):
     
     # Get endangerment status
     endangerment_status = get_status(predicted_class)
-
+    print(f"Status found: {endangerment_status}")
+    
     # Generate artistic interpretation
+    print("Sending request to FAL API...")
     result = fal_client.subscribe(
         "fal-ai/flux/schnell",
         arguments={
@@ -129,15 +132,19 @@ def process_image(img):
         with_logs=True,
         on_queue_update=on_queue_update,
     )
-    
+    print("FAL API responded")
+
     # Retrieve 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
 
 
+
 # Function to clear all outputs
 def clear_outputs():
     return {