gardenbirds / app.py
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Update app.py
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import torch
#import streamlit as st
from torchvision import transforms
import torch
import json
from torch import nn
from torchvision import models
from PIL import Image
from torch.nn import functional as F
import gradio as gr
# load idx_to_class.json into dictionary
with open('class_to_idx.json', 'r') as f:
class_to_idx = json.load(f)
idx_to_class = {v: k for k, v in class_to_idx.items()}
with open('gardenbirds_sorted.json', 'r') as f:
common_to_latin = json.load(f)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# model
model = models.resnet18(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, len(idx_to_class))
model.load_state_dict(torch.load('gardenbirds.pth', map_location=torch.device('cpu')))
model.eval()
data_transforms = {
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
def predict(inp):
# transform pil image to tensor
inp = data_transforms['val'](inp).unsqueeze(0)
with torch.no_grad():
prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
#print(prediction)
confidences = {list(common_to_latin.keys())[i]: float(prediction[i]) for i in range(32)}
return confidences
interface = gr.Interface(fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=3),
title="What garden bird is this?",
examples=["parus_major.jpg", "kingfisher.jpg", "wren.jpeg",
"carrion_crow.jpeg", "willow_warbler.jpeg", "goldcrest.jpeg"])
interface.launch()