app.py

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://cdn.i-scmp.com/sites/default/files/images/methode/2016/05/06/078562fe-1286-11e6-95eb-aaf30b46b489_image_hires.JPG') no-repeat center center fixed;
        background-size: cover;
    }
    .stApp h1 {
        background: linear-gradient(to right, #4b6cb7, #182848);
        color: white;
        padding: 15px 25px;
        border-radius: 15px;
        font-size: 2.5em;
        text-align: center;
        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3);
        max-width: 90%;
        margin: 30px auto;
        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
    }
    /* 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: 100%;
        margin-left: auto;
        margin-right: auto;
        text-align: center;
    }
        </style>
    """,
    unsafe_allow_html=True
)
# Title
st.title("Brain Tumor Classification")
st.write("")

# 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()

st.markdown(
    """
    <div style='
        background: weight:
        font-weight: bold;
        font-size: 25px;
        color: black;
        margin-bottom: 10px;
    '>
        📤 Upload a Scan Image
    </div>
    """,
    unsafe_allow_html=True
)
# Image upload
uploaded_img = st.file_uploader("", type=["jpg", "jpeg", "png"])
if st.button("Submit"):
    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)
    
        # 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
        )
    else:
        st.markdown(
                """
                <div style='background-color: #f8d7da; padding: 10px; border-radius: 5px;'>
                    <h4 style='color: #721c24;'> ⚠️ Plese upload image </h4>
                </div>
                """,
                unsafe_allow_html=True
            )