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Brain_tummer_classification
File size: 3,123 Bytes
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import streamlit as st
import torch
import torch.nn as nn
from torchvision import transforms, datasets, models
from PIL import Image

st.markdown(
    """
    <style>
    /* Set background image for the entire app */
    .stApp {
        background: url('https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSmgSUM3cbGaWX4tPdO2TGEX0x52TkjyuhfaA&sahttps://wp.technologyreview.com/wp-content/uploads/2023/04/brain-decode2.jpeg') no-repeat center center fixed;
        background-size: cover;
    }
     .stApp h1 {
        background-color: rgba(0, 0, 128, 0.7); 
        color: #ffffff; 
        padding: 10px;
        border-radius: 5px;
        font-size: 2.2em; 
        text-align: center;
        white-space: nowrap; /* Prevents line break */
        overflow: hidden;
        text-overflow: ellipsis;
        max-width: 100%;
        margin: 0 auto;
    }
    /* Style for the button */
    .stButton>button {
        background-color: #4CAF50; /* Green */
        color: white;
        font-size: 1.2em;
        border-radius: 10px;
        padding: 10px 24px;
        border: none;
    }
    /* Center the button */
    .stButton {
        display: flex;
        justify-content: center;
    }
    /* Style for the output container */
    .output-container {
        background-color: lightpink;
        color: black;
        font-size: 1.5em;
        padding: 15px;
        border-radius: 10px;
        margin-top: 20px;
        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
        width: 200%;
        margin-left: auto;
        margin-right: auto;
        text-align: center;
    }
        </style>
    """,
    unsafe_allow_html=True
)
# Title
st.title("Brain Tumor Classification")

# Class names
class_names = ['glioma_tumor', 'meningioma_tumor', 'no_tumor', 'pituitary_tumor']

# Load pre-trained ResNet18 model
model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
num_of_classes = len(class_names)
num_of_features = model.fc.in_features
model.fc = nn.Linear(num_of_features, num_of_classes)

# Load trained model weights
model.load_state_dict(torch.load('resnet18_model (1).pth', map_location=torch.device('cpu')))
model.eval()

# Image upload
uploaded_img = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

if uploaded_img is not None:
    # Display uploaded image in a smaller size
    image = Image.open(uploaded_img)
    st.image(image, caption="Uploaded Image", width=200)  # Set width to reduce image size

    # Image transformations
    sample_transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.1776, 0.1776, 0.1776], std=[0.1735, 0.1735, 0.1735])
    ])

    # Apply transformations
    transformed_img = sample_transform(image).unsqueeze(0)

    # Model inference
    with torch.no_grad():
        pred = model(transformed_img).argmax(dim=1).item()
    
    # Stylish output box
    st.markdown(
        f"""
        <div class="output-container">
            🧠 <strong>Predicted Class:</strong> {class_names[pred]}
        </div>
        """,
        unsafe_allow_html=True
    )