Update app.py

app.py

@@ -243,6 +243,210 @@ async def process_single_dog(image):
         return explanation, image, buttons[0], buttons[1], buttons[2], gr.update(visible=True), initial_state
 
 
+# async def predict(image):
+#     if image is None:
+#         return "Please upload an image to start.", None, gr.update(visible=False, choices=[]), None
+
+#     try:
+#         if isinstance(image, np.ndarray):
+#             image = Image.fromarray(image)
+
+#         dogs = await detect_multiple_dogs(image)
+        
+#         color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
+#         buttons = []
+#         annotated_image = image.copy()
+#         draw = ImageDraw.Draw(annotated_image)
+#         font = ImageFont.load_default()
+
+#         dogs_info = ""
+
+#         for i, (cropped_image, detection_confidence, box) in enumerate(dogs):
+#             buttons_html = ""  
+#             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] + 5, box[1] + 5) f"Dog {i+1}", fill=color, font=font)
+        
+#             combined_confidence = detection_confidence * top1_prob
+#             dogs_info += f'<div class="dog-info" style="border-left: 5px solid {color}; margin-bottom: 20px; padding: 15px;">'
+#             dogs_info += f'<h2>Dog {i+1}</h2>'
+        
+#             if top1_prob >= 0.45:
+#                 breed = topk_breeds[0]
+#                 description = get_dog_description(breed)
+#                 dogs_info += format_description_html(description, breed)
+        
+#             elif combined_confidence >= 0.15:
+#                 dogs_info += f"<p>Top 3 possible breeds:</p><ul>"
+#                 for j, (breed, prob) in enumerate(zip(topk_breeds[:3], topk_probs_percent[:3])):
+#                     prob = float(prob.replace('%', ''))
+#                     dogs_info += f"<li><strong>{breed}</strong> ({prob:.2f}% confidence)</li>"
+#                 dogs_info += "</ul>"
+        
+#                 for breed in topk_breeds[:3]:
+#                     button_id = f"Dog {i+1}: More about {breed}"
+#                     buttons_html += f'<button class="breed-button" onclick="handle_button_click(\'{button_id}\')">{breed}</button>'
+#                     buttons.append(button_id)
+        
+#             else:
+#                 dogs_info += "<p>The image is unclear or the breed is not in the dataset. Please upload a clearer image.</p>"
+        
+#             dogs_info += '</div>'    
+
+        
+#         buttons_html = ""  
+      
+#         html_output = f"""
+#         <style>
+#         .dog-info {{ border: 1px solid #ddd; margin-bottom: 20px; padding: 15px; border-radius: 5px; box-shadow: 0 2px 5px rgba(0,0,0,0.1); }}
+#         .dog-info h2 {{ background-color: #f0f0f0; padding: 10px; margin: -15px -15px 15px -15px; border-radius: 5px 5px 0 0; }}
+#         .breed-buttons {{ margin-top: 10px; }}
+#         .breed-button {{ margin-right: 10px; margin-bottom: 10px; padding: 5px 10px; background-color: #4CAF50; color: white; border: none; border-radius: 3px; cursor: pointer; }}
+#         </style>
+#         {dogs_info}
+#         """     
+
+#         if buttons:
+#             html_output += """
+#             <script>
+#             function handle_button_click(button_id) {
+#                 const radio = document.querySelector('input[type=radio][value="' + button_id + '"]');
+#                 if (radio) {
+#                     radio.click();
+#                 } else {
+#                     console.error("Radio button not found:", button_id);
+#                 }
+#             }
+#             </script>
+#             """
+#             initial_state = {
+#                 "dogs_info": dogs_info,
+#                 "buttons": buttons,
+#                 "show_back": True,
+#                 "image": annotated_image,
+#                 "is_multi_dog": len(dogs) > 1,
+#                 "html_output": html_output  
+#             }
+#             return html_output, annotated_image, gr.update(visible=True, choices=buttons), initial_state
+#         else:
+#             initial_state = {
+#                 "dogs_info": dogs_info,
+#                 "buttons": [],
+#                 "show_back": False,
+#                 "image": annotated_image,
+#                 "is_multi_dog": len(dogs) > 1,
+#                 "html_output": html_output  
+#             }
+#             return html_output, annotated_image, gr.update(visible=False, choices=[]), initial_state
+
+
+#     except Exception as e:
+#         error_msg = f"An error occurred: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+#         print(error_msg)
+#         return error_msg, None, gr.update(visible=False, choices=[]), None
+
+
+# def show_details_html(choice, previous_output, initial_state):
+#     if not choice:
+#         return previous_output, gr.update(visible=True), initial_state
+
+#     try:
+#         breed = choice.split("More about ")[-1]
+#         description = get_dog_description(breed)
+#         formatted_description = format_description_html(description, breed)
+        
+#         html_output = f"""
+#         <div class="dog-info">
+#             <h2>{breed}</h2>
+#             {formatted_description}
+#         </div>
+#         """
+        
+#         initial_state["current_description"] = html_output
+#         initial_state["original_buttons"] = initial_state.get("buttons", [])
+        
+#         return html_output, gr.update(visible=True), initial_state
+#     except Exception as e:
+#         error_msg = f"An error occurred while showing details: {e}"
+#         print(error_msg)
+#         return f"<p style='color: red;'>{error_msg}</p>", gr.update(visible=True), initial_state
+
+
+# def format_description_html(description, breed):
+#     html = "<ul style='list-style-type: none; padding-left: 0;'>"
+#     if isinstance(description, dict):
+#         for key, value in description.items():
+#             html += f"<li style='margin-bottom: 10px;'><strong>{key}:</strong> {value}</li>"
+#     elif isinstance(description, str):
+#         html += f"<li>{description}</li>"
+#     else:
+#         html += f"<li>No description available for {breed}</li>"
+#     html += "</ul>"
+#     akc_link = get_akc_breeds_link()
+#     html += f'<p><a href="{akc_link}" target="_blank">Learn more about {breed} on the AKC website</a></p>'
+#     return html
+
+
+# def go_back(state):
+#     buttons = state.get("buttons", [])
+#     return (
+#         state["html_output"],
+#         state["image"],
+#         gr.update(visible=True, choices=buttons),
+#         gr.update(visible=False),
+#         state
+#     )
+
+
+# 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.HTML(label="Prediction Results")  
+    
+#     breed_buttons = gr.Radio(choices=[], label="More Information", visible=False)
+    
+#     back_button = gr.Button("Back", visible=False)
+    
+#     initial_state = gr.State()
+    
+#     input_image.change(
+#         predict,
+#         inputs=input_image,
+#         outputs=[output, output_image, breed_buttons, initial_state]
+#     )
+
+#     breed_buttons.change(
+#         show_details_html,
+#         inputs=[breed_buttons, output, initial_state],
+#         outputs=[output, back_button, initial_state]
+#     )
+
+#     back_button.click(
+#         go_back,
+#         inputs=[initial_state],
+#         outputs=[output, output_image, breed_buttons, back_button, initial_state]
+#     )
+    
+#     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, choices=[]), None
@@ -261,41 +465,6 @@ async def predict(image):
 
         dogs_info = ""
 
-        # for i, (cropped_image, detection_confidence, box) in enumerate(dogs):
-        #     buttons_html = ""  
-        #     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] + 5, box[1] + 5) f"Dog {i+1}", fill=color, font=font)
-        
-        #     combined_confidence = detection_confidence * top1_prob
-        #     dogs_info += f'<div class="dog-info" style="border-left: 5px solid {color}; margin-bottom: 20px; padding: 15px;">'
-        #     dogs_info += f'<h2>Dog {i+1}</h2>'
-        
-        #     if top1_prob >= 0.45:
-        #         breed = topk_breeds[0]
-        #         description = get_dog_description(breed)
-        #         dogs_info += format_description_html(description, breed)
-        
-        #     elif combined_confidence >= 0.15:
-        #         dogs_info += f"<p>Top 3 possible breeds:</p><ul>"
-        #         for j, (breed, prob) in enumerate(zip(topk_breeds[:3], topk_probs_percent[:3])):
-        #             prob = float(prob.replace('%', ''))
-        #             dogs_info += f"<li><strong>{breed}</strong> ({prob:.2f}% confidence)</li>"
-        #         dogs_info += "</ul>"
-        
-        #         for breed in topk_breeds[:3]:
-        #             button_id = f"Dog {i+1}: More about {breed}"
-        #             buttons_html += f'<button class="breed-button" onclick="handle_button_click(\'{button_id}\')">{breed}</button>'
-        #             buttons.append(button_id)
-        
-        #     else:
-        #         dogs_info += "<p>The image is unclear or the breed is not in the dataset. Please upload a clearer image.</p>"
-        
-        #     dogs_info += '</div>'    
-
-
-
         for i, (cropped_image, detection_confidence, box) in enumerate(dogs):
             buttons_html = ""  
             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(cropped_image)
@@ -320,7 +489,8 @@ async def predict(image):
                     # 每個品種名稱後面立即生成按鈕
                     button_id = f"Dog {i+1}: More about {breed}"
                     dogs_info += f"<li><strong>{breed}</strong> ({prob:.2f}% confidence)"
-                    dogs_info += f'<button style="background-color: #4CAF50; color: white; border: none; padding: 5px 10px; border-radius: 3px; margin-left: 10px;" onclick="alert(\'{breed} clicked\')">Learn More</button></li>'
+                    # 新增按鈕，觸發相應事件
+                    dogs_info += f'<button style="background-color: #4CAF50; color: white; border: none; padding: 5px 10px; border-radius: 3px; margin-left: 10px;" onclick="handle_button_click(\'{button_id}\')">Learn More</button></li>'
                     buttons.append(button_id)
                 dogs_info += "</ul>"
         
@@ -330,11 +500,6 @@ async def predict(image):
             dogs_info += '</div>'  # 結束當前狗的資訊區塊
 
 
-
-
-         
-        buttons_html = ""  
-      
         html_output = f"""
         <style>
         .dog-info {{ border: 1px solid #ddd; margin-bottom: 20px; padding: 15px; border-radius: 5px; box-shadow: 0 2px 5px rgba(0,0,0,0.1); }}
@@ -345,19 +510,21 @@ async def predict(image):
         {dogs_info}
         """     
 
-        if buttons:
-            html_output += """
-            <script>
-            function handle_button_click(button_id) {
-                const radio = document.querySelector('input[type=radio][value="' + button_id + '"]');
-                if (radio) {
-                    radio.click();
-                } else {
-                    console.error("Radio button not found:", button_id);
-                }
+        # 生成腳本來處理按鈕點擊事件
+        html_output += """
+        <script>
+        function handle_button_click(button_id) {
+            const radio = document.querySelector('input[type=radio][value="' + button_id + '"]');
+            if (radio) {
+                radio.click();  // 點擊按鈕後觸發事件
+            } else {
+                console.error("Radio button not found:", button_id);
             }
-            </script>
-            """
+        }
+        </script>
+        """
+
+        if buttons:
             initial_state = {
                 "dogs_info": dogs_info,
                 "buttons": buttons,
@@ -478,7 +645,5 @@ 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()