Update app.py

app.py

@@ -31,48 +31,44 @@ architecture_description_yolov8s = """
 """
 
 description_yolov5 = """
-#### **1. Feature Focus for Dogs:**  
-- The model primarily focuses on:  
-  - **Face and Snout:** Key identifiers like eyes, nose, and mouth for dog recognition.  
-  - **Ears:** Distinctive ear shapes, such as pointed or floppy ears, are highlighted.  
-  - **Body Shape:** Focuses on the overall body contour, including legs and tails.  
-  - **Fur Texture:** Highlights curly fur in poodles and smooth fur in corgis.  
-
-#### **2. Feature Focus for Cats:**  
-- The network highlights:  
-  - **Face and Eyes:** Sharp eyes and whiskers as distinguishing features.  
-  - **Ears:** Pointy ears emphasized for identification.  
-  - **Body Size and Posture:** Compact body shape and sitting posture are captured.  
-  - **Tail and Paws:** Occasionally highlights tails or paws for finer details.  
-
-#### **3. Incorrect Focus or Ambiguity:**  
-- Some panels show **misfocused areas**, such as:  
-  - **Background:** Irrelevant parts receive attention, causing confusion.  
-  - **Shared Features:** Overlap in fur patterns or body shapes may lead to errors.  
-
-#### **4. Insights:**  
-- The visualizations explain **why the model classifies certain parts as important** for distinguishing dogs and cats.  
-- They highlight **potential biases** or areas for improvement, e.g., reliance on shared features instead of species-specific traits.  
+**Feature Focus for Dogs:**
+- **Face & Snout**: Eyes, nose, and mouth for recognition.
+- **Ears**: Pointed or floppy shapes.
+- **Body Shape**: Legs, tail, and contour.
+- **Fur Texture**: Curly (poodles) or smooth (corgis).
+
+**Feature Focus for Cats:**
+- **Face & Eyes**: Sharp eyes and whiskers.
+- **Ears**: Pointed for identification.
+- **Body Size & Posture**: Compact shape and sitting pose.
+- **Tail & Paws**: Sometimes highlighted.
+
+**Incorrect Focus:**
+- **Background**: Irrelevant areas confused with key features.
+- **Shared Features**: Overlap in fur or body shape causing errors.
+
+**Insights:**
+- Visualizations reveal important traits for classification, showing potential biases or areas for improvement. 
 """
 
 description_yolov8 = """
 
-#### **1. Feature Focus for Dogs:**  
+**Feature Focus for Dogs:** 
 - **Facial Features:**  Strong emphasis on **eyes, nose, and mouth**, aiding in species identification.  
 - **Ears and Fur Texture:** Highlights distinctive **fluffy or smooth fur patterns** and **ear shapes** (pointed or floppy).  
 - **Body and Legs:** Focus on the **overall contour, legs, and tails**, distinguishing body structures of breeds like poodles and corgis.  
 
-#### **2. Feature Focus for Cats:**  
+**Feature Focus for Cats:** 
 - **Face and Eyes:**  Sharp focus on **eyes and whiskers**, key characteristics of feline features.  
 - **Body Shape and Tail:**  Emphasizes the **compact size and tail curvature**.  
 - **Paws and Posture:**  Captures sitting postures and detailed paw structures.  
 
-#### **3. Incorrect Focus or Ambiguity:**  
+**Incorrect Focus:** 
 - **Background Highlights:** Some heatmaps show attention to **background regions**, leading to irrelevant feature extraction.  
 - **Shared Features:** Overlapping fur patterns and body structures between dogs and cats occasionally result in misclassification risks.  
 - **Edge and Border Effects:** Emphasis on **image boundaries** could reflect dataset biases during training.  
 
-#### **4. Insights:**  
+**Insights:**
 - Refinements, such as **attention-based mechanisms**, can improve focus on discriminative features and reduce errors.  
 """