Update app.py

app.py

@@ -267,6 +267,9 @@ def get_akc_breeds_link():
 #     iface.launch()
 
 def predict(image):
+    if image is None:
+        return "Please upload an image to get started.", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+    
     try:
         image_tensor = preprocess_image(image)
         with torch.no_grad():
@@ -281,22 +284,19 @@ def predict(image):
             topk_probs_percent = [f"{prob.item() * 100:.2f}%" for prob in topk_probs[0]]
 
             if top1_prob >= 0.5:
-                # High confidence prediction
                 breed = topk_breeds[0]
                 description = get_dog_description(breed)
                 return format_description(description, breed), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
             elif top1_prob < 0.1:
-                # Very low confidence prediction
                 return ("The image is too unclear or the dog breed is not in the dataset. Please upload a clearer image of the dog.",
                         gr.update(visible=False), gr.update(visible=False), gr.update(visible=False))
             else:
-                # Medium confidence prediction
                 explanation = (
-                    f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n"
-                    f"1. {topk_breeds[0]} ({topk_probs_percent[0]} confidence)\n"
-                    f"2. {topk_breeds[1]} ({topk_probs_percent[1]} confidence)\n"
-                    f"3. {topk_breeds[2]} ({topk_probs_percent[2]} confidence)\n\n"
+                    f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n"
+                    f"1. **{topk_breeds[0]}** ({topk_probs_percent[0]} confidence)\n"
+                    f"2. **{topk_breeds[1]}** ({topk_probs_percent[1]} confidence)\n"
+                    f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]} confidence)\n\n"
                     "Click on a button to view more information about the breed."
                 )
                 return explanation, gr.update(visible=True, value=f"More about {topk_breeds[0]}"), gr.update(visible=True, value=f"More about {topk_breeds[1]}"), gr.update(visible=True, value=f"More about {topk_breeds[2]}")
@@ -306,24 +306,25 @@ def predict(image):
 
 def format_description(description, breed):
     if isinstance(description, dict):
-        description_str = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()])
+        formatted_description = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()])
     else:
-        description_str = description
+        formatted_description = description
 
     akc_link = get_akc_breeds_link()
-    description_str += f"\n\n**Want to learn more about dog breeds?** [Visit the AKC dog breeds page]({akc_link}) and search for {breed} to find detailed information."
+    formatted_description += f"\n\n**Want to learn more about dog breeds?** [Visit the AKC dog breeds page]({akc_link}) and search for {breed} to find detailed information."
 
     disclaimer = ("\n\n*Disclaimer: The external link provided leads to the American Kennel Club (AKC) dog breeds page. "
                   "You may need to search for the specific breed on that page. "
                   "I am not responsible for the content on external sites. "
                   "Please refer to the AKC's terms of use and privacy policy.*")
-    description_str += disclaimer
+    formatted_description += disclaimer
 
-    return description_str
+    return formatted_description
 
 def show_details(breed):
-    description = get_dog_description(breed)
-    return format_description(description, breed)
+    breed_name = breed.split("More about ")[-1]
+    description = get_dog_description(breed_name)
+    return format_description(description, breed_name)
 
 with gr.Blocks(css="""
     .container {