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import streamlit as st
import torch
from PIL import Image
from torchvision import transforms
# Load your model (ensure this is the correct path to your model file)
@st.cache(allow_output_mutation=True)
def load_model():
model = torch.load('pretrained_vit_model_full.pth', map_location=torch.device('cpu'))
model.eval()
return model
model = load_model()
# Function to apply transforms to the image (update as per your model's requirement)
def transform_image(image):
transform = transforms.Compose([
transforms.Resize((224, 224)), # Resize to the input size that your model expects
transforms.ToTensor(),
# Add other transformations as needed
])
return transform(image).unsqueeze(0) # Add batch dimension
st.title("Animal Facial Expression Recognition")
# Slider
x = st.slider('Select a value')
st.write(x, 'squared is', x * x)
# File uploader
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
image = Image.open(uploaded_file).convert('RGB')
st.image(image, caption='Uploaded Image.', use_column_width=True)
st.write("")
st.write("Classifying...")
# Transform the image
input_tensor = transform_image(image)
# Make prediction
with torch.no_grad():
prediction = model(input_tensor)
# Display the prediction (modify as per your output)
st.write('Predicted class:', prediction.argmax().item())
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