space app 🐦

app.py

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+from gradio import inputs
+import torch
+import numpy as np
+import torchvision as tv
+import gradio as gr
+
+model = tv.models.efficientnet_b0()
+num_ftrs = model.classifier[1].in_features
+model.classifier[1] = torch.nn.Linear(num_ftrs, 21)
+model.load_state_dict(torch.load('model/model_ep=1_acc=0.8909620610367893.pt', map_location = torch.device('cpu')))
+model.eval()
+
+classes_to_idx = {'Accenteur mouchet': 0,
+ 'Bouvreuil pivoine': 1,
+ 'Chardonneret élégant': 2,
+ 'Ecureuil roux': 3,
+ 'Geai des chênes': 4,
+ 'Grosbec casse-noyaux': 5,
+ 'Merle noir': 6,
+ 'Moineau domestique': 7,
+ 'Moineau friquet': 8,
+ 'Mésange Nonnette': 9,
+ 'Mésange bleue': 10,
+ 'Mésange charbonnière': 11,
+ 'Mésange huppée': 12,
+ 'Mésange noire': 13,
+ 'Pic épeiche': 14,
+ 'Pinson des arbres': 15,
+ 'Pinson du Nord': 16,
+ 'Rougegorge familier': 17,
+ 'Sittelle torchepot': 18,
+ 'Tourterelle turque': 19,
+ "Verdier d'Europe": 20}
+
+classes = list(classes_to_idx.keys())
+
+preprocess = tv.transforms.Compose([
+    tv.transforms.Resize((270, 359)),
+    tv.transforms.ToTensor()
+    #tv.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+from PIL import Image
+
+def classidy_bird(image):
+
+    inputs = preprocess(image).unsqueeze(0)
+    inputs = inputs.to(torch.device('cpu'))
+
+    pred = torch.nn.functional.softmax(model(inputs), dim = 1).detach().numpy()[0]
+    return {classes[i] : float(pred[i]) for i in range(21)}
+
+image = gr.inputs.Image(type="pil", shape=(270, 359))
+label = gr.outputs.Label(num_top_classes=3)
+title = "Poids Plume Classifier"
+examples = ['examples/mesange-charbonniere.jpg', 'examples/merle-noir.jpg', 'examples/tourterelle-turque.jpg']
+
+gr.Interface(fn = classidy_bird, inputs=image, outputs=label, capture_session=True, examples=examples, title=title).launch()

model/model_ep=1_acc=0.8909620610367893.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0ae2ca74aef980ab47a865654129df1b57e84de5d7da9746862f4f590f82c920
+size 16434993