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90c960d 421b6e5 965e1b1 90c960d 965e1b1 67d0f13 421b6e5 965e1b1 67d0f13 421b6e5 32e35ec 421b6e5 67d0f13 90c960d 421b6e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | import streamlit as st
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
from torchvision.models import resnet18
import os
# Get the directory where the current script (app.py) is located
# Since app.py is in /app/src/ and the model is in /app/
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
MODEL_PATH = os.path.join(BASE_DIR, "resnet18_cifar10_finetuned.pth")
# Use MODEL_PATH in your load_model function
# Example: model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
# ---------------- Constants ----------------
CIFAR10_CLASSES = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
# ---------------- Model Loader ----------------
@st.cache_resource
def load_model():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = resnet18(pretrained=False)
model.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
model.maxpool = nn.Identity()
in_ftrs = model.fc.in_features
model.fc = nn.Sequential(
nn.Linear(in_ftrs, in_ftrs),
nn.ReLU(),
nn.Dropout(p=0.5),
nn.Linear(in_ftrs, 10)
)
model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
model.to(device)
model.eval()
return model, device
# ---------------- Preprocessing ----------------
def preprocess_image(image):
transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
std=[0.2023, 0.1994, 0.2010])
])
return transform(image).unsqueeze(0)
# ---------------- UI ----------------
st.title("🎯 CIFAR-10 Image Classifier")
st.write("Upload an image to classify it.")
st.write("ResNet18 model finetuned for 3 epochs with 95.6% accuracy on CIFAR10 images.")
st.write("The model performs well on images from CIFAR10 dataset.")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file:
try:
image = Image.open(uploaded_file).convert('RGB')
st.image(image, caption="Uploaded Image", width=200)
model, device = load_model()
with st.spinner("Classifying..."):
tensor = preprocess_image(image).to(device)
with torch.no_grad():
outputs = model(tensor)
probabilities = torch.softmax(outputs, dim=1)
confidence, predicted = torch.max(probabilities, 1)
st.success(f"Predicted: {CIFAR10_CLASSES[predicted.item()]}")
st.info(f"Confidence: {confidence.item()*100:.2f}%")
except Exception as e:
import traceback
st.error("An error occurred:")
st.text(traceback.format_exc())
top5_probs, top5_indices = torch.topk(probabilities, 5)
st.subheader("Top 5 Predictions")
for i in range(5):
label = CIFAR10_CLASSES[top5_indices[0][i].item()]
prob = top5_probs[0][i].item() * 100
st.write(f"{i+1}. {label} – {prob:.2f}%")
st.write("Done.")
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