Update app.py

app.py

@@ -301,120 +301,9 @@ def _detect_multiple_dogs(image, conf_threshold):
     return dogs
 
 
-# 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)
-
-#     try:
-#         if isinstance(image, np.ndarray):
-#             image = Image.fromarray(image)
-
-#         # 嘗試檢測多隻狗
-#         dogs = await detect_multiple_dogs(image)
-#         if len(dogs) == 0:
-#             # 單狗情境
-#             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)
-
-#             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)
-#             else:
-#                 explanation = (
-#                     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, image, 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]}")
-
-#         # 多狗情境
-#         color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
-#         explanations = []
-#         annotated_image = image.copy()
-#         draw = ImageDraw.Draw(annotated_image)
-#         font = ImageFont.load_default()
-
-#         for i, (cropped_image, _, box) in enumerate(dogs):
-#             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(cropped_image)
-#             color = color_list[i % len(color_list)]
-#             draw.rectangle(box, outline=color, width=3)
-#             draw.text((box[0], box[1]), f"Dog {i+1}", fill=color, font=font)
-
-#             breed = topk_breeds[0]
-#             if top1_prob >= 0.5:
-#                 description = get_dog_description(breed)
-#                 formatted_description = format_description(description, breed)
-#                 explanations.append(f"Dog {i+1}: {formatted_description}")
-#             elif top1_prob >= 0.2:
-#                 explanations.append(f"Dog {i+1}: 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)")
-#             else:
-#                 explanations.append(f"Dog {i+1}: The image is unclear or the breed is not in the dataset.")
-
-#         final_explanation = "\n\n".join(explanations)
-#         return final_explanation, annotated_image, gr.update(visible=False), 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)
-
-# def show_details(choice):
-#     if not choice:
-#         return "Please select a breed to view details."
-
-#     try:
-#         breed = choice.split("More about ")[-1]
-#         description = get_dog_description(breed)
-#         return format_description(description, breed)
-#     except Exception as e:
-#         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)
+        return "Please upload an image to start.", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
     try:
         if isinstance(image, np.ndarray):
@@ -424,12 +313,29 @@ async def predict(image):
         dogs = await detect_multiple_dogs(image)
         if len(dogs) == 0:
             # 單狗情境
-            return await 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)
+
+            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)
+            else:
+                explanation = (
+                    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, image, 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]}")
 
         # 多狗情境
         color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
         explanations = []
-        choices = []
         annotated_image = image.copy()
         draw = ImageDraw.Draw(annotated_image)
         font = ImageFont.load_default()
@@ -440,63 +346,37 @@ async def predict(image):
             draw.rectangle(box, outline=color, width=3)
             draw.text((box[0], box[1]), f"Dog {i+1}", fill=color, font=font)
 
-            if top1_prob < 0.2:
-                explanations.append(f"Dog {i+1}: The image is unclear or the breed is not in the dataset. Please upload a clearer image of the dog.")
-            elif top1_prob >= 0.5:
-                breed = topk_breeds[0]
+            breed = topk_breeds[0]
+            if top1_prob >= 0.5:
                 description = get_dog_description(breed)
                 formatted_description = format_description(description, breed)
                 explanations.append(f"Dog {i+1}: {formatted_description}")
+            elif top1_prob >= 0.2:
+                explanations.append(f"Dog {i+1}: 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)")
             else:
-                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}: {breed}" for breed in topk_breeds[:3]])
+                explanations.append(f"Dog {i+1}: The image is unclear or the breed is not in the dataset.")
 
         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)
-        else:
-            return final_explanation, annotated_image, gr.update(visible=False), gr.update(visible=False)
+        return final_explanation, annotated_image, gr.update(visible=False), 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)
-
-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)
-
-    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)
-    else:
-        explanation = (
-            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."
-        )
-        choices = [f"{breed}" for breed in topk_breeds[:3]]
-        return explanation, image, gr.update(visible=True, choices=choices), gr.update(visible=False)
+        return f"An error occurred: {str(e)}", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
 def show_details(choice):
     if not choice:
         return "Please select a breed to view details."
 
     try:
-        breed = choice.split(": ")[-1]  # 處理可能的 "Dog X: " 前綴
+        breed = choice.split("More about ")[-1]
         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>")
@@ -506,20 +386,21 @@ with gr.Blocks() as iface:
         output_image = gr.Image(label="Annotated Image")
     
     output = gr.Markdown(label="Prediction Results")
-    breed_choices = gr.Radio([], label="Select breed for more details", visible=False)
-    breed_details = gr.Markdown(label="Breed Details")
+    
+    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, breed_choices, breed_details]
+        outputs=[output, output_image, btn1, btn2, btn3]
     )
 
-    breed_choices.change(
-        show_details,
-        inputs=breed_choices,
-        outputs=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)
 
     gr.Examples(
         examples=['Border_Collie.jpg', 'Golden_Retriever.jpeg', 'Saint_Bernard.jpeg', 'French_Bulldog.jpeg', 'Samoyed.jpg'],
@@ -529,4 +410,4 @@ with gr.Blocks() as iface:
     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()
+    iface.launch()