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| import streamlit as st | |
| import torch | |
| import torchvision.transforms as transforms | |
| from torchvision.transforms import ToTensor, Normalize, Compose | |
| from PIL import Image | |
| import numpy as np | |
| from galaxy_classification import GalaxyCNN # Modelini import et | |
| # Streamlit sayfa yapılandırması | |
| st.set_page_config( | |
| page_title="Galaxy Morphology Classification", | |
| page_icon="✨", | |
| layout="wide", | |
| initial_sidebar_state="expanded", | |
| ) | |
| # Modeli yükle | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = GalaxyCNN().to(device) | |
| model.load_state_dict(torch.load("galaxy_cnn_model.pt", map_location=device)) | |
| model.eval() | |
| # Görsel işleme fonksiyonu | |
| def process_image(image): | |
| transform = Compose([ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) # 3 kanal için normalize | |
| image = image.convert("RGB").resize((64, 64)) # Modelin beklediği boyutta olmalı | |
| image = transform(image).unsqueeze(0).to(device) # Model için uygun forma getir | |
| return image | |
| # Başlık ve alt başlık | |
| st.title("🔭 Galaxy Morphology Classifier") | |
| st.subheader("Upload a galaxy image and predict its morphological class!") | |
| # Açıklama | |
| st.write(""" | |
| ### How It Works | |
| 1. Upload a galaxy image. | |
| 2. Click the "Classify Image" button. | |
| 3. The model predicts the galaxy's morphological class. | |
| """) | |
| # Görsel yükleme alanı | |
| uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file: | |
| image = Image.open(uploaded_file) | |
| st.image(image, caption="Uploaded Image", use_column_width=True) | |
| # Tahmin butonu | |
| if st.button("🔎 Classify Image"): | |
| # Görseli işle | |
| processed_image = process_image(image) | |
| # Model ile tahmin yap | |
| with torch.no_grad(): | |
| output = model(processed_image) | |
| predicted_class = torch.argmax(output, dim=1).item() | |
| # Sınıf isimleri | |
| class_names = ["Smooth Round", "Smooth Cigar", "Edge-on Disk", "Spiral"] | |
| st.write(f"**Prediction:** {class_names[predicted_class]} 🎯") | |