Update app.py

app.py

@@ -266,6 +266,145 @@ 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
@@ -277,7 +416,7 @@ async def predict(image):
         dogs = await detect_multiple_dogs(image)
         
         color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
-        explanations = []
+        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;}</style>"
         buttons = []
         annotated_image = image.copy()
         draw = ImageDraw.Draw(annotated_image)
@@ -287,82 +426,105 @@ async def predict(image):
             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
-
+            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)
-                formatted_description = format_description(description, breed)
-                explanations.append(f"Dog {i+1}: {formatted_description}")
+                html_output += format_description_html(description, breed)
             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]])
+                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 onclick="handle_button_click(\'{button_id}\')">{button_id}</button>'
+                    buttons.append(button_id)
+                html_output += '</div>'
             else:
-                explanations.append(f"{i+1} The image is unclear or the breed is not in the dataset. Please upload a clearer image.")
-                
+                html_output += "<p>The image is unclear or the breed is not in the dataset. Please upload a clearer image.</p>"
+            
+            html_output += '</div>'
 
-        final_explanation = "\n\n".join(explanations)
         if buttons:
-            final_explanation += "\n\nClick on a button to view more information about the breed."
+            html_output += "<script>function handle_button_click(button_id) { document.querySelector('input[type=radio][value=\"' + button_id + '\"]').click(); }</script>"
             initial_state = {
-                "explanation": final_explanation,
+                "explanation": html_output,
                 "buttons": buttons,
                 "show_back": True,
                 "image": annotated_image,
                 "is_multi_dog": len(dogs) > 1,
-                "dogs_info": explanations
+                "dogs_info": html_output
             }
-            return final_explanation, annotated_image, gr.update(visible=True, choices=buttons), initial_state
+            return html_output, annotated_image, gr.update(visible=True, choices=buttons), initial_state
         else:
             initial_state = {
-                "explanation": final_explanation,
+                "explanation": html_output,
                 "buttons": [],
                 "show_back": False,
                 "image": annotated_image,
                 "is_multi_dog": len(dogs) > 1,
-                "dogs_info": explanations
+                "dogs_info": html_output
             }
-            return final_explanation, annotated_image, gr.update(visible=False, choices=[]), initial_state
+            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(choice, previous_output, initial_state):
+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(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"] = formatted_description
+        initial_state["current_description"] = html_output
         initial_state["original_buttons"] = initial_state.get("buttons", [])
         
-        return formatted_description, gr.update(visible=True), initial_state
+        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 error_msg, gr.update(visible=True), initial_state
+        return f"<p style='color: red;'>{error_msg}</p>", gr.update(visible=True), initial_state
+
+def format_description_html(description, breed):
+    html = "<ul>"
+    for key, value in description.items():
+        html += f"<li><strong>{key}:</strong> {value}</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["explanation"],
+        state["dogs_info"],  # 這裡應該是完整的 HTML 內容
         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>")
@@ -371,7 +533,7 @@ with gr.Blocks() as iface:
         input_image = gr.Image(label="Upload a dog image", type="pil")
         output_image = gr.Image(label="Annotated Image")
     
-    output = gr.Markdown(label="Prediction Results")
+    output = gr.HTML(label="Prediction Results")  # 改為 HTML 輸出
     
     breed_buttons = gr.Radio(choices=[], label="More Information", visible=False)
     
@@ -404,5 +566,6 @@ 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()