Update app.py

app.py

@@ -134,25 +134,6 @@ def get_akc_breeds_link():
     return "https://www.akc.org/dog-breeds/"
 
 
-# def format_description(description, breed):
-#     if isinstance(description, dict):
-#         # 確保每一個描述項目換行顯示
-#         formatted_description = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()])
-#     else:
-#         formatted_description = description
-
-#     akc_link = get_akc_breeds_link()
-#     formatted_description += f"\n\n**Want to learn more about dog breeds?** [Visit the AKC dog breeds page]({akc_link}) and search for {breed} to find detailed information."
-
-#     disclaimer = ("\n\n*Disclaimer: The external link provided leads to the American Kennel Club (AKC) dog breeds page. "
-#                   "You may need to search for the specific breed on that page. "
-#                   "I am not responsible for the content on external sites. "
-#                   "Please refer to the AKC's terms of use and privacy policy.*")
-#     formatted_description += disclaimer
-
-#     return formatted_description
-
-
 async def predict_single_dog(image):
     image_tensor = preprocess_image(image)
     with torch.no_grad():
@@ -266,242 +247,6 @@ 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']
-#         explanations = []
-#         buttons = []
-#         annotated_image = image.copy()
-#         draw = ImageDraw.Draw(annotated_image)
-#         font = ImageFont.load_default()
-
-#         for i, (cropped_image, detection_confidence, 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] + 5, box[1] + 5), f"Dog {i+1}", fill=color, font=font) # Adjust the mark place
-
-
-#             combined_confidence = detection_confidence * top1_prob
-
-#             if top1_prob >= 0.45:
-#                 breed = topk_breeds[0]
-#                 description = get_dog_description(breed)
-#                 formatted_description = format_description(description, breed)
-#                 explanations.append(f"Dog {i+1}: {formatted_description}")
-#             elif combined_confidence >= 0.15:
-#                 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([f"Dog {i+1}: More about {breed}" for breed in topk_breeds[:3]])
-#             else:
-#                 explanations.append(f"{i+1} The image is unclear or the breed is not in the dataset. Please upload a clearer image.")
-                
-
-#         final_explanation = "\n\n".join(explanations)
-#         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,
-#                 "image": annotated_image,
-#                 "is_multi_dog": len(dogs) > 1,
-#                 "dogs_info": explanations
-#             }
-#             return final_explanation, annotated_image, gr.update(visible=True, choices=buttons), initial_state
-#         else:
-#             initial_state = {
-#                 "explanation": final_explanation,
-#                 "buttons": [],
-#                 "show_back": False,
-#                 "image": annotated_image,
-#                 "is_multi_dog": len(dogs) > 1,
-#                 "dogs_info": explanations
-#             }
-#             return final_explanation, 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(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(description, breed)
-        
-#         initial_state["current_description"] = formatted_description
-#         initial_state["original_buttons"] = initial_state.get("buttons", [])
-        
-#         return formatted_description, gr.update(visible=True), initial_state
-#     except Exception as e:
-#         error_msg = f"An error occurred while showing details: {e}"
-#         print(error_msg)
-#         return error_msg, gr.update(visible=True), initial_state
-
-# def go_back(state):
-#     buttons = state.get("buttons", [])
-#     return (
-#         state["explanation"],
-#         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.Markdown(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,
-#         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>')
-
-
-# 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']
-#         html_output = """
-#         <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>
-#         """
-#         buttons = []
-#         annotated_image = image.copy()
-#         draw = ImageDraw.Draw(annotated_image)
-#         font = ImageFont.load_default()
-
-#         for i, (cropped_image, detection_confidence, 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] + 5, box[1] + 5), f"Dog {i+1}", fill=color, font=font)
-    
-#             combined_confidence = detection_confidence * top1_prob
-#             html_output += f'<div class="dog-info" style="border-left: 5px solid {color};">'
-#             html_output += f'<h2>Dog {i+1}</h2>'
-
-#             if top1_prob >= 0.45:
-#                 breed = topk_breeds[0]
-#                 description = get_dog_description(breed)
-#                 html_output += f"<p><strong>{breed}</strong> ({top1_prob:.2%} confidence)</p>"
-#                 html_output += format_description_html(description, breed)
-#                 button_id = f"Dog {i+1}: More about {breed}"
-#                 html_output += f'<div class="breed-buttons"><button class="breed-button" onclick="handle_button_click(\'{button_id}\')">{breed}</button></div>'
-#                 buttons.append(button_id)
-#             elif combined_confidence >= 0.15:
-#                 html_output += f"<p>Top 3 possible breeds:</p><ul>"
-#                 for j, (breed, prob) in enumerate(zip(topk_breeds[:3], topk_probs_percent[:3])):
-#                     html_output += f"<li><strong>{breed}</strong> ({prob} confidence)</li>"
-#                 html_output += "</ul>"
-#                 html_output += '<div class="breed-buttons">'
-#                 for breed in topk_breeds[:3]:
-#                     button_id = f"Dog {i+1}: More about {breed}"
-#                     html_output += f'<button class="breed-button" onclick="handle_button_click(\'{button_id}\')">{breed}</button>'
-#                     buttons.append(button_id)
-#                 html_output += '</div>'
-#             else:
-#                 html_output += "<p>The image is unclear or the breed is not in the dataset. Please upload a clearer image.</p>"
-            
-#             html_output += '</div>'
-
-#         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 = {
-#                 "explanation": html_output,
-#                 "buttons": buttons,
-#                 "show_back": True,
-#                 "image": annotated_image,
-#                 "is_multi_dog": len(dogs) > 1,
-#                 "dogs_info": html_output
-#             }
-#             return html_output, annotated_image, gr.update(visible=True, choices=buttons), initial_state
-#         else:
-#             initial_state = {
-#                 "explanation": html_output,
-#                 "buttons": [],
-#                 "show_back": False,
-#                 "image": annotated_image,
-#                 "is_multi_dog": len(dogs) > 1,
-#                 "dogs_info": 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
-
-
 async def predict(image):
     if image is None:
         return "Please upload an image to start.", None, gr.update(visible=False, choices=[]), None
@@ -539,7 +284,8 @@ async def predict(image):
             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])):
-                    dogs_info += f"<li><strong>{breed}</strong> ({prob} confidence)</li>"
+                    #dogs_info += f"<li><strong>{breed}</strong> ({prob} confidence)</li>"
+                    dogs_info += f"<li><strong>{breed}</strong> ({prob:.2f}% confidence)</li>"
                 dogs_info += "</ul>" 
                 buttons_html = '<div class="breed-buttons">'  #new
                 for breed in topk_breeds[:3]: