Fix the text in the desc

app.py

@@ -1,85 +1,85 @@
-import gradio as gr
-import os
-import torch
-
-#from model import create_densenet_model
-import model
-from timeit import default_timer as timer
-
-
-import os
-from pathlib import Path
-
-
-from pathlib import Path
-
-# 1. Get the path of the current folder where app.py is located
-current_dir = Path(__file__).parent 
-
-# 2. Load class names from the text file
-# This fixes the "File Not Found" error by using the full path
-with open(current_dir / "class_names.txt", "r") as f:
-    class_names = [line.strip() for line in f.readlines()]
-
-# Verify the number of classes loaded
-print(f"Loaded {len(class_names)} classes.")
-
-model_1, transforms = model.create_model(num_classes=120)
-
-
-state_dict = torch.load(
-    f="30_epoch_model_efficientv2_2_93%_acc_dog_bread_classifier.pth",
-    weights_only=False,
-    map_location="cpu"
-)
-
-model_1.load_state_dict(state_dict)
-
-
-def predict_img(img):
-
-  start_time = timer()
-
-  img = transforms(img).unsqueeze(0)
-
-  model_1.eval()
-
-  with torch.inference_mode():
-
-    # 1. Get the probabilities
-    pred_probs = torch.softmax(model_1(img), dim=1)
-
-    # 2. Create a dictionary of ALL classes and their probabilities
-    all_pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
-
-    # 3. Sort them by value (probability) in descending order and take the first 5
-    pred_labels_and_probs = dict(sorted(all_pred_labels_and_probs.items(), 
-                                    key=lambda item: item[1], 
-                                    reverse=True)[:5])
-
-   #pred_time = round(timer() - start_time(),5)
-    pred_time = round(timer() - start_time, 5)
-
-    return pred_labels_and_probs, pred_time
-
-title = "Covid Lung Classified"
-description = "Dog Breed Classifier"
-article = "Created at 2026"
-
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-demo = gr.Interface(
-    fn=predict_img,
-    inputs=gr.Image(type="pil"),
-    outputs=[
-        gr.Label(num_top_classes=5,label="Predictions"),
-        gr.Number(label="Prediction Time")
-    ],
-    examples = example_list,
-    title = title,
-    description = description,
-    article = article
-)
-
-
+import gradio as gr
+import os
+import torch
+
+#from model import create_densenet_model
+import model
+from timeit import default_timer as timer
+
+
+import os
+from pathlib import Path
+
+
+from pathlib import Path
+
+# 1. Get the path of the current folder where app.py is located
+current_dir = Path(__file__).parent 
+
+# 2. Load class names from the text file
+# This fixes the "File Not Found" error by using the full path
+with open(current_dir / "class_names.txt", "r") as f:
+    class_names = [line.strip() for line in f.readlines()]
+
+# Verify the number of classes loaded
+print(f"Loaded {len(class_names)} classes.")
+
+model_1, transforms = model.create_model(num_classes=120)
+
+
+state_dict = torch.load(
+    f="30_epoch_model_efficientv2_2_93%_acc_dog_bread_classifier.pth",
+    weights_only=False,
+    map_location="cpu"
+)
+
+model_1.load_state_dict(state_dict)
+
+
+def predict_img(img):
+
+  start_time = timer()
+
+  img = transforms(img).unsqueeze(0)
+
+  model_1.eval()
+
+  with torch.inference_mode():
+
+    # 1. Get the probabilities
+    pred_probs = torch.softmax(model_1(img), dim=1)
+
+    # 2. Create a dictionary of ALL classes and their probabilities
+    all_pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+
+    # 3. Sort them by value (probability) in descending order and take the first 5
+    pred_labels_and_probs = dict(sorted(all_pred_labels_and_probs.items(), 
+                                    key=lambda item: item[1], 
+                                    reverse=True)[:5])
+
+   #pred_time = round(timer() - start_time(),5)
+    pred_time = round(timer() - start_time, 5)
+
+    return pred_labels_and_probs, pred_time
+
+title = "Dog Breed Classifier"
+description = "Upload a photo of your dog here to identify its breed! Our AI analyzes 120 different types to give you the top 5 most likely matches in seconds. Simply drag and drop your image, click submit, and see the results. Fast, fun, and accurate dog breed classification at your fingertips."
+article = "Created at Mauaque Ressettlement Center Gozales Compound"
+
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+demo = gr.Interface(
+    fn=predict_img,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Label(num_top_classes=5,label="Predictions"),
+        gr.Number(label="Prediction Time")
+    ],
+    examples = example_list,
+    title = title,
+    description = description,
+    article = article
+)
+
+
 demo.launch(debug=True)