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README.md

@@ -7,3 +7,6 @@ sdk_version: 4.44.1
 license: mit
 pinned:  true
 ---
+
+
+# DogBreedsClassifier

app.py

@@ -1,70 +1,79 @@
-import os 
+import os
 import torch
-import numpy as np
 import lightning as pl
-import gradio as gr 
+import gradio as gr
 from PIL import Image
 from torchvision import transforms
-from timeit import default_timer as timer 
+from timeit import default_timer as timer
 from torch.nn import functional as F
 
 torch.set_float32_matmul_precision('medium')
 device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-torch.set_default_device( device= device  )
-torch.autocast(enabled = True,dtype='float16',device_type='cuda')
+torch.set_default_device(device=device)
+torch.autocast(enabled=True, dtype='float16', device_type='cuda')
 
 pl.seed_everything(123, workers=True)
 
 TEST_TRANSFORMS = transforms.Compose([
-            transforms.Resize((224, 224)),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-        ])
-class_labels= ['Beagle', 'Boxer', 'Bulldog', 'Dachshund', 'German_Shepherd', 'Golden_Retriever','Labrador_Retriever', 'Poodle','Rottweiler','Yorkshire_Terrier']
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+class_labels = [
+    'Beagle',
+    'Boxer',
+    'Bulldog',
+    'Dachshund',
+    'German_Shepherd',
+    'Golden_Retriever',
+    'Labrador_Retriever',
+    'Poodle',
+    'Rottweiler',
+    'Yorkshire_Terrier',
+]
 
 
 # Model
-model = torch.jit.load('best_model.pt',map_location=device).to(device)
+model = torch.jit.load('best_model.pt', map_location=device).to(device)
 
 
 @torch.no_grad()
-def predict_fn(img:Image):
+def predict_fn(img: Image):
     start_time = timer()
-    try:    
-        # img = np.array(img)   
-        # print(img)     
+    try:
+        # img = np.array(img)
+        # print(img)
         img = TEST_TRANSFORMS(img).to(device)
         # print(type(img),img.shape)
         logits = model(img.unsqueeze(0))
-        probabilities = F.softmax(logits,dim=-1)
+        probabilities = F.softmax(logits, dim=-1)
         # print(torch.topk(probabilities,k=2))
         y_pred = probabilities.argmax(dim=-1).item()
         confidence = probabilities[0][y_pred].item()
         predicted_label = class_labels[y_pred]
         # print(confidence,predicted_label)
-        pred_time = round(timer()-start_time,5)
-        res = {f"Title: {predicted_label}":confidence}
-        return (res,pred_time)
+        pred_time = round(timer() - start_time, 5)
+        res = {f'Title: {predicted_label}': confidence}
+        return (res, pred_time)
     except Exception as e:
-        print(f"error:: {e}")
-        gr.Error("An error occured 💥!", duration=5)
-        return ({ f"Title ☠️": 0.0},0.0)
-
-
-
+        print(f'error:: {e}')
+        gr.Error('An error occured 💥!', duration=5)
+        return ({'Title ☠️': 0.0}, 0.0)
 
 
 gr.Interface(
-        fn=predict_fn,
-        inputs=gr.Image(type='pil'),
-        outputs=[
-            gr.Label(num_top_classes=1, label="Predictions"), # what are the outputs?
-            gr.Number(label="Prediction time (s)")
-        ],
-        examples=[ ['examples/'+i] for i in os.listdir(os.path.join( os.path.dirname(__file__) ,'examples'))],
-        title="Dog Breeds Classifier 🐈",
-        description="CNN-based Architecture for Fast and Accurate DogsBreed Classifier",
-        article="Created by muthukamalan.m ❤️",
-        cache_examples=True,
-        ).launch(share=False,debug=False)
-
+    fn=predict_fn,
+    inputs=gr.Image(type='pil'),
+    outputs=[
+        gr.Label(num_top_classes=1, label='Predictions'),  # what are the outputs?
+        gr.Number(label='Prediction time (s)'),
+    ],
+    examples=[
+        ['examples/' + i]
+        for i in os.listdir(os.path.join(os.path.dirname(__file__), 'examples'))
+    ],
+    title='Dog Breeds Classifier 🐈',
+    description='CNN-based Architecture for Fast and Accurate DogsBreed Classifier',
+    article='Created by muthukamalan.m ❤️',
+    cache_examples=True,
+).launch(share=False, debug=False)