Update app.py

app.py

@@ -296,81 +296,80 @@ async def detect_multiple_dogs(image, conf_threshold=0.2, iou_threshold=0.5):
     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}"
+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:
@@ -409,7 +408,6 @@ async def detect_multiple_dogs(image, conf_threshold=0.2, iou_threshold=0.5):
 #     iface.launch()
 
 
-
 # 介面部分
 with gr.Blocks() as iface:
     gr.HTML("<h1 style='text-align: center;'>🐶 Dog Breed Classifier 🔍</h1>")