Update app.py

app.py

@@ -132,19 +132,6 @@ def get_akc_breeds_link(breed):
     return f"{base_url}{breed_url}/"
 
 
-# async def predict_single_dog(image):
-#     image_tensor = preprocess_image(image)
-#     with torch.no_grad():
-#         output = model(image_tensor)
-#         logits = output[0] if isinstance(output, tuple) else output
-#         probabilities = F.softmax(logits, dim=1)
-#         topk_probs, topk_indices = torch.topk(probabilities, k=3)
-#         top1_prob = topk_probs[0][0].item()
-#         topk_breeds = [dog_breeds[idx.item()] for idx in topk_indices[0]]
-#         topk_probs_percent = [f"{prob.item() * 100:.2f}%" for prob in topk_probs[0]]
-#     return top1_prob, topk_breeds, topk_probs_percent
-
-
 async def predict_single_dog(image):
     image_tensor = preprocess_image(image)
     with torch.no_grad():
@@ -200,6 +187,7 @@ def non_max_suppression(boxes, iou_threshold):
         boxes = [box for box in boxes if calculate_iou(current[0], box[0]) < iou_threshold]
     return keep
 
+    
 def calculate_iou(box1, box2):
     x1 = max(box1[0], box2[0])
     y1 = max(box1[1], box2[1])
@@ -214,160 +202,6 @@ def calculate_iou(box1, box2):
     return iou
 
 
-
-# async def process_single_dog(image):
-#     top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
-#     if top1_prob < 0.15:
-#         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,
-#             "image": None,
-#             "is_multi_dog": 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 >= 0.45:
-#         formatted_description = format_description(description, breed)
-#         initial_state = {
-#             "explanation": formatted_description,
-#             "buttons": [],
-#             "show_back": False,
-#             "image": image,
-#             "is_multi_dog": 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,
-#             "image": image,
-#             "is_multi_dog": False
-#         }
-#         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
-
-
-
 async def process_single_dog(image):
     top1_prob, topk_breeds, relative_probs = await predict_single_dog(image)
     
@@ -536,64 +370,6 @@ def format_description_html(description, breed):
     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()
-
-
-
 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 and provide detailed information!</p>")