Update app.py

app.py

@@ -251,107 +251,97 @@ def get_akc_breeds_link():
 #     iface.launch()
 
 
-# 使用 YOLOv8 進行狗偵測
 def detect_dogs(image):
-    results = yolo_model.predict(image)
+    results = yolo_model(image)
     dogs = []
-
     for result in results:
         for box in result.boxes:
-            if box.cls == 16:  # COCO 資料集中的狗類別是16
-                xyxy = box.xyxy
-                confidence = box.conf
+            if box.cls == 16:  # COCO 資料集中的狗類別是 16
+                xyxy = box.xyxy[0].tolist()
+                confidence = box.conf.item()
                 cropped_image = image.crop((xyxy[0], xyxy[1], xyxy[2], xyxy[3]))
                 dogs.append((cropped_image, confidence, xyxy))
-
     return dogs
 
+def predict_breed(cropped_image):
+    image_tensor = preprocess_image(cropped_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
+
 def predict(image):
     if image is None:
-        return "Please upload an image to get started.", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+        return "請上傳一張圖片來開始。", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
     try:
-        # 確保圖片轉換為 PIL.Image 格式
         if isinstance(image, np.ndarray):
             image = Image.fromarray(image)
 
-        # 使用 YOLO 偵測狗
         dogs = detect_dogs(image)
         if len(dogs) == 0:
-            return "No dogs detected or the image is too unclear. Please upload a clearer image.", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+            return "未檢測到狗或圖片不清晰。請上傳一張更清晰的狗的圖片。", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
-        # 開始處理每一隻狗
         explanations = []
         visible_buttons = []
         annotated_image = image.copy()
-
-        for i, (cropped_image, confidence, box) in enumerate(dogs):
-            image_tensor = preprocess_image(cropped_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]]
-
-                # 標註狗的邊界框
-                draw = ImageDraw.Draw(annotated_image)
-                draw.rectangle(box.tolist(), outline="red", width=3)
-                draw.text((box[0], box[1]), f"Dog {i+1}", fill="red")
-
-                # 信心度大於 50%，顯示詳細品種資訊
-                if top1_prob >= 0.5:
-                    breed = topk_breeds[0]
-                    description = get_dog_description(breed)
-                    explanations.append(f"Dog {i+1}: **{breed}**\n{format_description(description, breed)}")
-                # 信心度 20%-49%，顯示 Top 3 品種
-                elif 0.2 <= top1_prob < 0.5:
-                    explanation = (
-                        f"Dog {i+1}: Detected with moderate confidence. Here are the top 3 possible breeds:\n"
-                        f"1. **{topk_breeds[0]}** ({topk_probs_percent[0]})\n"
-                        f"2. **{topk_breeds[1]}** ({topk_probs_percent[1]})\n"
-                        f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]})\n"
-                    )
-                    explanations.append(explanation)
-                    visible_buttons.extend([f"More about {topk_breeds[0]}", f"More about {topk_breeds[1]}", f"More about {topk_breeds[2]}"])
-                else:
-                    explanations.append(f"Dog {i+1}: The image is unclear or the breed is not in the dataset.")
+        draw = ImageDraw.Draw(annotated_image)
+
+        for i, (cropped_image, _, box) in enumerate(dogs):
+            top1_prob, topk_breeds, topk_probs_percent = predict_breed(cropped_image)
+
+            draw.rectangle(box, outline="red", width=3)
+            draw.text((box[0], box[1]), f"狗 {i+1}", fill="red")
+
+            if top1_prob >= 0.5:
+                breed = topk_breeds[0]
+                description = get_dog_description(breed)
+                explanations.append(f"狗 {i+1}: **{breed}**\n{format_description(description, breed)}")
+            elif 0.2 <= top1_prob < 0.5:
+                explanation = (
+                    f"狗 {i+1}: 中等置信度檢測。以下是前3個可能的品種：\n"
+                    f"1. **{topk_breeds[0]}** ({topk_probs_percent[0]})\n"
+                    f"2. **{topk_breeds[1]}** ({topk_probs_percent[1]})\n"
+                    f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]})\n"
+                )
+                explanations.append(explanation)
+                visible_buttons.extend([f"更多關於 {topk_breeds[0]}", f"更多關於 {topk_breeds[1]}", f"更多關於 {topk_breeds[2]}"])
+            else:
+                explanations.append(f"狗 {i+1}: 圖片不清晰或品種不在數據集中。")
 
         final_explanation = "\n\n".join(explanations)
-        return annotated_image, final_explanation, gr.update(visible=len(visible_buttons) >= 1), gr.update(visible=len(visible_buttons) >= 2), gr.update(visible=len(visible_buttons) >= 3)
+        return final_explanation, annotated_image, gr.update(visible=len(visible_buttons) >= 1, value=visible_buttons[0] if visible_buttons else ""), gr.update(visible=len(visible_buttons) >= 2, value=visible_buttons[1] if len(visible_buttons) >= 2 else ""), gr.update(visible=len(visible_buttons) >= 3, value=visible_buttons[2] if len(visible_buttons) >= 3 else "")
 
     except Exception as e:
-        return f"An error occurred: {e}", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-
+        return f"發生錯誤：{e}", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
 def format_description(description, breed):
     if isinstance(description, dict):
-        formatted_description = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()])
+        formatted_description = "\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."
+    formatted_description += f"\n\n**想了解更多狗品種資訊？** [訪問 AKC 狗品種頁面]({akc_link})並搜尋 {breed} 以獲取詳細資訊。"
 
-    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.*")
+    disclaimer = ("\n\n*免責聲明：提供的外部連結指向美國養犬俱樂部（AKC）的狗品種頁面。"
+                  "您可能需要在該頁面上搜索特定品種。"
+                  "我對外部網站的內容不負責任。"
+                  "請參閱 AKC 的使用條款和隱私政策。*")
     formatted_description += disclaimer
 
     return formatted_description
 
 def show_details(breed):
-    breed_name = breed.split("More about ")[-1]
+    breed_name = breed.split("更多關於 ")[-1]
     description = get_dog_description(breed_name)
     return format_description(description, breed_name)
 
-
 with gr.Blocks(css="""
     .container {
         max-width: 900px;
@@ -383,20 +373,21 @@ with gr.Blocks(css="""
     }
     """) as iface:
 
-    gr.HTML("<h1 style='font-family:Roboto; font-weight:bold; color:#2C3E50; text-align:center;'>🐶 Dog Breed Classifier 🔍</h1>")
-    gr.HTML("<p style='font-family:Open Sans; color:#34495E; 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>")
+    gr.HTML("<h1 style='font-family:Roboto; font-weight:bold; color:#2C3E50; text-align:center;'>🐶 狗狗品種分類器 🔍</h1>")
+    gr.HTML("<p style='font-family:Open Sans; color:#34495E; text-align:center;'>上傳一張狗狗的照片，模型將預測其品種，提供詳細資訊，並包含額外的資訊連結！</p>")
     
     with gr.Row():
-        input_image = gr.Image(label="Upload a dog image", type="numpy")
-        output_image = gr.Image(label="Annotated Image")
-        output = gr.Markdown(label="Prediction Results")
+        input_image = gr.Image(label="上傳狗狗圖片", type="pil")
+        output_image = gr.Image(label="標註後的圖片")
+    
+    output = gr.Markdown(label="預測結果")
     
     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)
+        btn1 = gr.Button("查看更多 1", visible=False)
+        btn2 = gr.Button("查看更多 2", visible=False)
+        btn3 = gr.Button("查看更多 3", visible=False)
 
-    input_image.change(predict, inputs=input_image, outputs=[output_image, output, btn1, btn2, btn3])
+    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)
@@ -407,10 +398,8 @@ with gr.Blocks(css="""
         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%20Breed%20Classifier">Dog Breed Classifier</a>')
+    gr.HTML('如需了解本項目的更多詳情和其他作品，歡迎訪問我的 GitHub <a href="https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/Dog%20Breed%20Classifier">狗狗品種分類器</a>')
 
-# launch the program
 if __name__ == "__main__":
     iface.launch()
 
-