Update app.py

app.py

@@ -1,134 +1,134 @@
-import numpy as np
-import pandas as pd
-import yaml
-import os
-
-import torch
-import torchvision
-from torchvision.transforms import v2
-import PIL
-from PIL import Image
-
-import gradio as gr
-
-def app_interface():
-    with open("model_names.yaml") as config:
-        config__ = yaml.safe_load(config)
-    model_1 = config__["models"]["vit_model"]
-    def return_classes_():
-        classes_ = {0: 'adonis',
-                    1: 'african giant swallowtail',
-                    2: 'american snoot',
-                    3: 'an 88',
-                    4: 'appollo',
-                    5: 'atala',
-                    6: 'banded orange heliconian',
-                    7: 'banded peacock',
-                    8: 'beckers white',
-                    9: 'black hairstreak',
-                    10: 'blue morpho',
-                    11: 'blue spotted crow',
-                    12: 'brown siproeta',
-                    13: 'cabbage white',
-                    14: 'cairns birdwing',
-                    15: 'checquered skipper',
-                    16: 'chestnut',
-                    17: 'cleopatra',
-                    18: 'clodius parnassian',
-                    19: 'clouded sulphur',
-                    20: 'common banded awl',
-                    21: 'common wood-nymph',
-                    22: 'copper tail',
-                    23: 'crecent',
-                    24: 'crimson patch',
-                    25: 'danaid eggfly',
-                    26: 'eastern coma',
-                    27: 'eastern dapple white',
-                    28: 'eastern pine elfin',
-                    29: 'elbowed pierrot',
-                    30: 'gold banded',
-                    31: 'great eggfly',
-                    32: 'great jay',
-                    33: 'green celled cattleheart',
-                    34: 'grey hairstreak',
-                    35: 'indra swallow',
-                    36: 'iphiclus sister',
-                    37: 'julia',
-                    38: 'large marble',
-                    39: 'malachite',
-                    40: 'mangrove skipper',
-                    41: 'mestra',
-                    42: 'metalmark',
-                    43: 'milberts tortoiseshell',
-                    44: 'monarch',
-                    45: 'mourning cloak',
-                    46: 'orange oakleaf',
-                    47: 'orange tip',
-                    48: 'orchard swallow',
-                    49: 'painted lady',
-                    50: 'paper kite',
-                    51: 'peacock',
-                    52: 'pine white',
-                    53: 'pipevine swallow',
-                    54: 'popinjay',
-                    55: 'purple hairstreak',
-                    56: 'purplish copper',
-                    57: 'question mark',
-                    58: 'red admiral',
-                    59: 'red cracker',
-                    60: 'red postman',
-                    61: 'red spotted purple',
-                    62: 'scarce swallow',
-                    63: 'silver spot skipper',
-                    64: 'sleepy orange',
-                    65: 'sootywing',
-                    66: 'southern dogface',
-                    67: 'straited queen',
-                    68: 'tropical leafwing',
-                    69: 'two barred flasher',
-                    70: 'ulyses',
-                    71: 'viceroy',
-                    72: 'wood satyr',
-                    73: 'yellow swallow tail',
-                    74: 'zebra long wing'}
-        return classes_
-    model = torch.load(f"models/{model_1}.pt", map_location=torch.device('cpu'))
-    def make_predictions(path_:str):
-        transform = v2.Compose([
-            v2.Resize((224,224)),
-            v2.ToImage(),
-            v2.ToDtype(torch.float32, scale = True),
-            v2.Normalize(mean=[0.485, 0.456, 0.406],
-                         std=[0.229, 0.224, 0.225])])
-        class_map = return_classes_()
-        model_ = torch.load(f"models/{model_1}.pt", map_location=torch.device('cpu'))
-        # img_ = Image.open(path_)
-        img__ = transform(path_).unsqueeze(dim = 0)
-        pred_ = model_(img__)
-        pred = torch.argmax(torch.softmax(pred_, dim = 1), axis = 1)
-        top_n = pd.DataFrame(data = torch.softmax(pred_, dim = 1).detach().numpy(), columns = class_map.values()).T
-        top_n.columns = ["probs"]
-        top_n = top_n.sort_values("probs", ascending = False)
-        top_n = top_n.loc[top_n.index[:3],:]
-        top_n_dict = {}
-        top_specie, top_prob = [i for i in top_n.index], [i for i in top_n.probs.values]
-        for i, j in zip(top_specie, top_prob):
-            top_n_dict[f"{i}"] = float(j)
-        # display(top_n)
-        # plt.imshow(img_)
-        # plt.grid(axis = "both", lw = 0)
-        # plt.title(f"{class_map[pred.item()].capitalize()} | {torch.softmax(pred_, dim = 1).max():.2f}", fontweight = "bold", fontsize = 10)
-        return top_n_dict, f"predicted specie of butterfly is => {class_map[pred.item()].capitalize()}; with a confidence of {torch.softmax(pred_, dim = 1).max()}%"
-    title = "ButterFly Specie Classifier"
-    description = "Vision Transformer  feature extractor model trained to classify species of butterflies"
-
-    demo = gr.Interface(fn = make_predictions, inputs = gr.Image(type = "pil", label = "Upload Image here"),
-                        outputs=[gr.Label(num_top_classes=3, label = "Predictions"), gr.Textbox(label = "Result")],
-                        title = title,
-                        description=description)
-
-    demo.launch(share = True)
-
-
-if __name__ == "__main__":
+import numpy as np
+import pandas as pd
+import yaml
+import os
+
+import torch
+import torchvision
+from torchvision.transforms import v2
+import PIL
+from PIL import Image
+
+import gradio as gr
+
+def app_interface():
+    with open("model_names.yaml") as config:
+        config__ = yaml.safe_load(config)
+    model_1 = config__["models"]["vit_model"]
+    def return_classes_():
+        classes_ = {0: 'adonis',
+                    1: 'african giant swallowtail',
+                    2: 'american snoot',
+                    3: 'an 88',
+                    4: 'appollo',
+                    5: 'atala',
+                    6: 'banded orange heliconian',
+                    7: 'banded peacock',
+                    8: 'beckers white',
+                    9: 'black hairstreak',
+                    10: 'blue morpho',
+                    11: 'blue spotted crow',
+                    12: 'brown siproeta',
+                    13: 'cabbage white',
+                    14: 'cairns birdwing',
+                    15: 'checquered skipper',
+                    16: 'chestnut',
+                    17: 'cleopatra',
+                    18: 'clodius parnassian',
+                    19: 'clouded sulphur',
+                    20: 'common banded awl',
+                    21: 'common wood-nymph',
+                    22: 'copper tail',
+                    23: 'crecent',
+                    24: 'crimson patch',
+                    25: 'danaid eggfly',
+                    26: 'eastern coma',
+                    27: 'eastern dapple white',
+                    28: 'eastern pine elfin',
+                    29: 'elbowed pierrot',
+                    30: 'gold banded',
+                    31: 'great eggfly',
+                    32: 'great jay',
+                    33: 'green celled cattleheart',
+                    34: 'grey hairstreak',
+                    35: 'indra swallow',
+                    36: 'iphiclus sister',
+                    37: 'julia',
+                    38: 'large marble',
+                    39: 'malachite',
+                    40: 'mangrove skipper',
+                    41: 'mestra',
+                    42: 'metalmark',
+                    43: 'milberts tortoiseshell',
+                    44: 'monarch',
+                    45: 'mourning cloak',
+                    46: 'orange oakleaf',
+                    47: 'orange tip',
+                    48: 'orchard swallow',
+                    49: 'painted lady',
+                    50: 'paper kite',
+                    51: 'peacock',
+                    52: 'pine white',
+                    53: 'pipevine swallow',
+                    54: 'popinjay',
+                    55: 'purple hairstreak',
+                    56: 'purplish copper',
+                    57: 'question mark',
+                    58: 'red admiral',
+                    59: 'red cracker',
+                    60: 'red postman',
+                    61: 'red spotted purple',
+                    62: 'scarce swallow',
+                    63: 'silver spot skipper',
+                    64: 'sleepy orange',
+                    65: 'sootywing',
+                    66: 'southern dogface',
+                    67: 'straited queen',
+                    68: 'tropical leafwing',
+                    69: 'two barred flasher',
+                    70: 'ulyses',
+                    71: 'viceroy',
+                    72: 'wood satyr',
+                    73: 'yellow swallow tail',
+                    74: 'zebra long wing'}
+        return classes_
+    model = torch.load(f"models/{model_1}.pt", map_location=torch.device('cpu'))
+    def make_predictions(path_:str):
+        transform = v2.Compose([
+            v2.Resize((224,224)),
+            v2.ToImage(),
+            v2.ToDtype(torch.float32, scale = True),
+            v2.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])])
+        class_map = return_classes_()
+        model_ = torch.load(f"models/{model_1}.pt", map_location=torch.device('cpu'))
+        # img_ = Image.open(path_)
+        img__ = transform(path_).unsqueeze(dim = 0)
+        pred_ = model_(img__)
+        pred = torch.argmax(torch.softmax(pred_, dim = 1), axis = 1)
+        top_n = pd.DataFrame(data = torch.softmax(pred_, dim = 1).detach().numpy(), columns = class_map.values()).T
+        top_n.columns = ["probs"]
+        top_n = top_n.sort_values("probs", ascending = False)
+        top_n = top_n.loc[top_n.index[:3],:]
+        top_n_dict = {}
+        top_specie, top_prob = [i for i in top_n.index], [i for i in top_n.probs.values]
+        for i, j in zip(top_specie, top_prob):
+            top_n_dict[f"{i}"] = float(j)
+        # display(top_n)
+        # plt.imshow(img_)
+        # plt.grid(axis = "both", lw = 0)
+        # plt.title(f"{class_map[pred.item()].capitalize()} | {torch.softmax(pred_, dim = 1).max():.2f}", fontweight = "bold", fontsize = 10)
+        return top_n_dict, f"predicted specie of butterfly is => {class_map[pred.item()].capitalize()}; with a confidence of {torch.softmax(pred_, dim = 1).max()}%"
+    title = "ButterFly Specie Classifier"
+    description = "Vision Transformer  feature extractor model trained to classify species of butterflies"
+
+    demo = gr.Interface(fn = make_predictions, inputs = gr.Image(type = "pil", label = "Upload Image here"),
+                        outputs=[gr.Label(num_top_classes=3, label = "Predictions"), gr.Textbox(label = "Result")],
+                        title = title,
+                        description=description)
+
+    demo.launch(share = False)
+
+
+if __name__ == "__main__":
     app_interface()