Upload 8 files

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+test_dataset.hdf5 filter=lfs diff=lfs merge=lfs -text
+train_dataset.hdf5 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +1,14 @@
 ---
-title: Galaxy Classification
-emoji: 📈
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.38.1
+title: Galaxy Classifier
+emoji: 🦀
+colorFrom: pink
+colorTo: red
+sdk: streamlit
+sdk_version: 1.42.1
 app_file: app.py
 pinned: false
-short_description: Galaxy Classifications
+license: mit
+short_description: Upload a galaxy image and get morphological class prediction
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,61 @@
+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]} 🎯")

galaxy_classification.py

@@ -0,0 +1,95 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, TensorDataset
+from torchvision.transforms import Compose, ToTensor, Normalize
+from sklearn.metrics import accuracy_score
+import h5py
+
+# Veriyi yüklemek için bir fonksiyon
+def load_hdf5_dataset(path):
+    with h5py.File(path, "r") as f:
+        images = torch.tensor(f['images'][:])  # Görseller
+        labels = torch.tensor(f['labels'][:])  # Etiketler
+    return images, labels
+
+# Verileri yükleme
+train_images, train_labels = load_hdf5_dataset('train_dataset.hdf5')
+test_images, test_labels = load_hdf5_dataset('test_dataset.hdf5')
+
+# Normalize etmek ve tensöre dönüştürmek için dönüşümler
+transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
+
+# Verileri normalize et ve TensorDataset oluştur
+train_images = train_images.permute(0, 3, 1, 2) / 255.0  # Görüntüleri 0-1 aralığına getir
+test_images = test_images.permute(0, 3, 1, 2) / 255.0
+train_dataset = TensorDataset(train_images, train_labels)
+test_dataset = TensorDataset(test_images, test_labels)
+
+# CNN Modelini Tanımlama
+class GalaxyCNN(nn.Module):
+    def __init__(self):
+        super(GalaxyCNN, self).__init__()
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.fc1 = nn.Linear(64 * 16 * 16, 128)  # Fully connected layer
+        self.fc2 = nn.Linear(128, 4)  # 4 sınıf (smooth_round, smooth_cigar, vb.)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(0.25)
+
+    def forward(self, x):
+        x = self.pool(self.relu(self.conv1(x)))
+        x = self.pool(self.relu(self.conv2(x)))
+        x = x.view(-1, 64 * 16 * 16)  # Flatten
+        x = self.dropout(self.relu(self.fc1(x)))
+        x = self.fc2(x)
+        return x
+
+# DataLoader
+batch_size = 32
+train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
+
+# Model ve cihaz
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = GalaxyCNN().to(device)
+
+# Kayıp fonksiyonu ve optimizasyon
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+# Eğitim
+epochs = 10
+for epoch in range(epochs):
+    model.train()
+    running_loss = 0.0
+    for images, labels in train_loader:
+        images, labels = images.to(device), labels.to(device)
+        optimizer.zero_grad()
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+        running_loss += loss.item()
+
+    avg_loss = running_loss / len(train_loader)
+    print(f"Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}")
+
+# Test ve doğrulama
+model.eval()
+all_preds = []
+all_labels = []
+with torch.no_grad():
+    for images, labels in test_loader:
+        images, labels = images.to(device), labels.to(device)
+        outputs = model(images)
+        _, preds = torch.max(outputs, 1)
+        all_preds.extend(preds.cpu().numpy())
+        all_labels.extend(labels.cpu().numpy())
+
+accuracy = accuracy_score(all_labels, all_preds)
+print(f"Validation Accuracy: {accuracy * 100:.2f}%")
+torch.save(model.state_dict(), "galaxy_cnn_model.pt")
+print("Model kaydedildi: galaxy_cnn_model.pt")
+

galaxy_cnn_model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d1b1cb8209c1516b54b5a4f5403ea7587de75d094c2a396750f316ab4f1e7fb8
+size 8472044

gitattributes

@@ -0,0 +1,37 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+test_dataset.hdf5 filter=lfs diff=lfs merge=lfs -text
+train_dataset.hdf5 filter=lfs diff=lfs merge=lfs -text

requirements.txt

@@ -0,0 +1,8 @@
+torch
+torchvision
+scikit-learn
+streamlit==1.18.1
+tensorflow==2.14.0
+Pillow==10.0.0
+numpy==1.24.3
+h5py==3.8.0

test_dataset.hdf5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95d53ca3e9c8fbf45150b77527cd1180c43e2423da0ff2e0fb0012f986e35901
+size 24580048

train_dataset.hdf5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e357f36269e4c2d056e3660b877f3f7dc8d17b1446906b1117124b55567a201
+size 98314048