Update app.py

app.py

@@ -296,41 +296,135 @@ 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}"
+
+
+# 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), gr.update(visible=False), gr.update(visible=False), None
 
     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]}")
+        dogs = await detect_multiple_dogs(image, conf_threshold=0.05)
+        
+        if len(dogs) <= 1:
+            return await process_single_dog(image)
 
         # 多狗情境
         color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
         explanations = []
+        buttons = []
         annotated_image = image.copy()
         draw = ImageDraw.Draw(annotated_image)
         font = ImageFont.load_default()
@@ -347,31 +441,96 @@ async def predict(image):
                 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)")
+                dog_explanation = f"Dog {i+1}: 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)
+                buttons.extend([gr.update(visible=True, value=f"Dog {i+1}: More about {breed}") for breed in topk_breeds[:3]])
             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)
+        if buttons:
+            final_explanation += "\n\nClick on a button to view more information about the breed."
+            initial_state = {
+                "explanation": final_explanation,
+                "buttons": buttons,
+                "show_back": True
+            }
+            return (final_explanation, annotated_image, 
+                    buttons[0] if len(buttons) > 0 else gr.update(visible=False),
+                    buttons[1] if len(buttons) > 1 else gr.update(visible=False),
+                    buttons[2] if len(buttons) > 2 else gr.update(visible=False),
+                    gr.update(visible=True),
+                    initial_state)
+        else:
+            initial_state = {
+                "explanation": final_explanation,
+                "buttons": [],
+                "show_back": False
+            }
+            return final_explanation, annotated_image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), initial_state
 
     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):
+        error_msg = f"An error occurred: {str(e)}"
+        print(error_msg)  # 添加日誌輸出
+        return error_msg, None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), None
+
+async def process_single_dog(image):
+    top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
+    if top1_prob < 0.2:
+        initial_state = {
+            "explanation": "The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.",
+            "buttons": [],
+            "show_back": False
+        }
+        return initial_state["explanation"], None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), initial_state
+
+    breed = topk_breeds[0]
+    description = get_dog_description(breed)
+
+    if top1_prob >= 0.5:
+        formatted_description = format_description(description, breed)
+        initial_state = {
+            "explanation": formatted_description,
+            "buttons": [],
+            "show_back": False
+        }
+        return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), initial_state
+    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."
+        )
+        buttons = [
+            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]}")
+        ]
+        initial_state = {
+            "explanation": explanation,
+            "buttons": buttons,
+            "show_back": True
+        }
+        return explanation, image, buttons[0], buttons[1], buttons[2], gr.update(visible=True), initial_state
+
+def show_details(choice, previous_output, initial_state):
     if not choice:
-        return "Please select a breed to view details."
+        return previous_output, gr.update(visible=True), initial_state
 
     try:
         breed = choice.split("More about ")[-1]
         description = get_dog_description(breed)
-        return format_description(description, breed)
+        formatted_description = format_description(description, breed)
+        return formatted_description, gr.update(visible=True), initial_state
     except Exception as e:
-        return f"An error occurred while showing details: {e}"
-
+        error_msg = f"An error occurred while showing details: {e}"
+        print(error_msg)  # 添加日誌輸出
+        return error_msg, gr.update(visible=True), initial_state
 
+# 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>")
@@ -386,16 +545,33 @@ with gr.Blocks() as iface:
         btn1 = gr.Button("View More 1", visible=False)
         btn2 = gr.Button("View More 2", visible=False)
         btn3 = gr.Button("View More 3", visible=False)
-
+    
+    back_button = gr.Button("Back", visible=False)
+    
+    initial_state = gr.State()
+    
     input_image.change(
         predict,
         inputs=input_image,
-        outputs=[output, output_image, btn1, btn2, btn3]
+        outputs=[output, output_image, btn1, btn2, btn3, back_button, initial_state]
     )
 
-    btn1.click(show_details, inputs=btn1, outputs=output)
-    btn2.click(show_details, inputs=btn2, outputs=output)
-    btn3.click(show_details, inputs=btn3, outputs=output)
+    for btn in [btn1, btn2, btn3]:
+        btn.click(
+            show_details,
+            inputs=[btn, output, initial_state],
+            outputs=[output, back_button, initial_state]
+        )
+
+    back_button.click(
+        lambda state: (state["explanation"], 
+                       state["buttons"][0] if len(state["buttons"]) > 0 else gr.update(visible=False),
+                       state["buttons"][1] if len(state["buttons"]) > 1 else gr.update(visible=False),
+                       state["buttons"][2] if len(state["buttons"]) > 2 else gr.update(visible=False),
+                       gr.update(visible=state["show_back"])),
+        inputs=[initial_state],
+        outputs=[output, btn1, btn2, btn3, back_button]
+    )
 
     gr.Examples(
         examples=['Border_Collie.jpg', 'Golden_Retriever.jpeg', 'Saint_Bernard.jpeg', 'French_Bulldog.jpeg', 'Samoyed.jpg'],
@@ -403,7 +579,3 @@ 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()
-