Update app.py

app.py

@@ -377,9 +377,44 @@ def _detect_multiple_dogs(image, conf_threshold):
 #         return f"An error occurred while showing details: {e}"
 
 
+# with gr.Blocks() as iface:
+#     gr.HTML("<h1 style='text-align: center;'>🐶 Dog Breed Classifier 🔍</h1>")
+#     gr.HTML("<p style='text-align: center;'>Upload a picture of a dog, and the model will predict its breed, provide detailed information, and include an extra information link!</p>")
+    
+#     with gr.Row():
+#         input_image = gr.Image(label="Upload a dog image", type="pil")
+#         output_image = gr.Image(label="Annotated Image")
+    
+#     output = gr.Markdown(label="Prediction Results")
+    
+#     with gr.Row():
+#         btn1 = gr.Button("View More 1", visible=False)
+#         btn2 = gr.Button("View More 2", visible=False)
+#         btn3 = gr.Button("View More 3", visible=False)
+
+#     input_image.change(
+#         predict,
+#         inputs=input_image,
+#         outputs=[output, output_image, btn1, btn2, btn3]
+#     )
+
+#     btn1.click(show_details, inputs=btn1, outputs=output)
+#     btn2.click(show_details, inputs=btn2, outputs=output)
+#     btn3.click(show_details, inputs=btn3, outputs=output)
+
+#     gr.Examples(
+#         examples=['Border_Collie.jpg', 'Golden_Retriever.jpeg', 'Saint_Bernard.jpeg', 'French_Bulldog.jpeg', 'Samoyed.jpg'],
+#         inputs=input_image
+#     )
+
+#     gr.HTML('For more details on this project and other work, feel free to visit my GitHub <a href="https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/Dog_Breed_Classifier">Dog Breed Classifier</a>')
+
+# if __name__ == "__main__":
+#     iface.launch()
+
 async def predict(image):
     if image is None:
-        return "Please upload an image to start.", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+        return "Please upload an image to start.", None, gr.update(visible=False), gr.update(visible=False)
 
     try:
         if isinstance(image, np.ndarray):
@@ -388,8 +423,8 @@ async def predict(image):
         # 嘗試檢測多隻狗
         dogs = await detect_multiple_dogs(image)
         if len(dogs) == 0:
-            # 單狗情境 - 保持不變
-            return process_single_dog(image)
+            # 單狗情境
+            return await process_single_dog(image)
 
         # 多狗情境
         color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
@@ -416,29 +451,29 @@ async def predict(image):
                 dog_explanation = f"Dog {i+1}: The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n"
                 dog_explanation += "\n".join([f"{j+1}. **{breed}** ({prob} confidence)" for j, (breed, prob) in enumerate(zip(topk_breeds[:3], topk_probs_percent[:3]))])
                 explanations.append(dog_explanation)
-                choices.extend([f"Dog {i+1}: More about {breed}" for breed in topk_breeds[:3]])
+                choices.extend([f"Dog {i+1}: {breed}" for breed in topk_breeds[:3]])
 
         final_explanation = "\n\n".join(explanations)
         if choices:
             final_explanation += "\n\nClick on a button to view more information about the breed."
-            return final_explanation, annotated_image, gr.update(visible=True, choices=choices), gr.update(visible=False), gr.update(visible=False)
+            return final_explanation, annotated_image, gr.update(visible=True, choices=choices), gr.update(visible=False)
         else:
-            return final_explanation, annotated_image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+            return final_explanation, annotated_image, gr.update(visible=False), gr.update(visible=False)
 
     except Exception as e:
-        return f"An error occurred: {str(e)}", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+        return f"An error occurred: {str(e)}", None, gr.update(visible=False), gr.update(visible=False)
 
-def process_single_dog(image):
+async def process_single_dog(image):
     top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
     if top1_prob < 0.2:
-        return "The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+        return "The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.", None, gr.update(visible=False), gr.update(visible=False)
 
     breed = topk_breeds[0]
     description = get_dog_description(breed)
 
     if top1_prob >= 0.5:
         formatted_description = format_description(description, breed)
-        return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+        return formatted_description, image, gr.update(visible=False), gr.update(visible=False)
     else:
         explanation = (
             f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n"
@@ -447,22 +482,21 @@ def process_single_dog(image):
             f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]} confidence)\n\n"
             "Click on a button to view more information about the breed."
         )
-        choices = [f"More about {breed}" for breed in topk_breeds[:3]]
-        return explanation, image, gr.update(visible=True, choices=choices), gr.update(visible=False), gr.update(visible=False)
+        choices = [f"{breed}" for breed in topk_breeds[:3]]
+        return explanation, image, gr.update(visible=True, choices=choices), gr.update(visible=False)
 
 def show_details(choice):
     if not choice:
         return "Please select a breed to view details."
 
     try:
-        breed = choice.split("More about ")[-1]
+        breed = choice.split(": ")[-1]  # 處理可能的 "Dog X: " 前綴
         description = get_dog_description(breed)
         return format_description(description, breed)
     except Exception as e:
         return f"An error occurred while showing details: {e}"
 
-
-
+# 介面部分
 with gr.Blocks() as iface:
     gr.HTML("<h1 style='text-align: center;'>🐶 Dog Breed Classifier 🔍</h1>")
     gr.HTML("<p style='text-align: center;'>Upload a picture of a dog, and the model will predict its breed, provide detailed information, and include an extra information link!</p>")
@@ -472,21 +506,20 @@ with gr.Blocks() as iface:
         output_image = gr.Image(label="Annotated Image")
     
     output = gr.Markdown(label="Prediction Results")
-    
-    with gr.Row():
-        btn1 = gr.Button("View More 1", visible=False)
-        btn2 = gr.Button("View More 2", visible=False)
-        btn3 = gr.Button("View More 3", visible=False)
+    breed_choices = gr.Radio([], label="Select breed for more details", visible=False)
+    breed_details = gr.Markdown(label="Breed Details")
 
     input_image.change(
         predict,
         inputs=input_image,
-        outputs=[output, output_image, btn1, btn2, btn3]
+        outputs=[output, output_image, breed_choices, breed_details]
     )
 
-    btn1.click(show_details, inputs=btn1, outputs=output)
-    btn2.click(show_details, inputs=btn2, outputs=output)
-    btn3.click(show_details, inputs=btn3, outputs=output)
+    breed_choices.change(
+        show_details,
+        inputs=breed_choices,
+        outputs=breed_details
+    )
 
     gr.Examples(
         examples=['Border_Collie.jpg', 'Golden_Retriever.jpeg', 'Saint_Bernard.jpeg', 'French_Bulldog.jpeg', 'Samoyed.jpg'],