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.gitattributes

@@ -33,3 +33,8 @@ 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
+Screenshot[[:space:]]2026-01-31[[:space:]]150516.png filter=lfs diff=lfs merge=lfs -text
+Screenshot[[:space:]]2026-01-31[[:space:]]150636.png filter=lfs diff=lfs merge=lfs -text
+Screenshot[[:space:]]2026-01-31[[:space:]]150840.png filter=lfs diff=lfs merge=lfs -text
+Screenshot[[:space:]]2026-01-31[[:space:]]151118.png filter=lfs diff=lfs merge=lfs -text
+Screenshot[[:space:]]2026-01-31[[:space:]]151244.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,126 @@
+import gradio as gr
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import joblib
+import torchvision.transforms as transforms
+from PIL import Image
+
+class STL10Net(nn.Module):
+    def __init__(self):
+        super(STL10Net, self).__init__()        
+        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.bn1 = nn.BatchNorm2d(32)
+        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
+        self.bn2 = nn.BatchNorm2d(64)
+        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
+        self.bn3 = nn.BatchNorm2d(128)
+        self.conv4 = nn.Conv2d(128, 256, 3, padding=1)
+        self.bn4 = nn.BatchNorm2d(256)
+        self.fc1 = nn.Linear(256 * 6 * 6, 512) 
+        self.dropout = nn.Dropout(0.5)
+        self.fc2 = nn.Linear(512, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.bn1(self.conv1(x))))
+        x = self.pool(F.relu(self.bn2(self.conv2(x))))
+        x = self.pool(F.relu(self.bn3(self.conv3(x))))
+        x = self.pool(F.relu(self.bn4(self.conv4(x))))
+        x = x.view(-1, 256 * 6 * 6)
+        x = F.relu(self.fc1(x))
+        x = self.dropout(x)
+        x = self.fc2(x)
+        return x
+
+
+original_torch_load = torch.load
+
+def cpu_load(*args, **kwargs):
+    kwargs['map_location'] = torch.device('cpu')
+    return original_torch_load(*args, **kwargs)
+
+
+torch.load = cpu_load 
+try:
+    model = joblib.load('stl10_cnn_model.pkl')
+finally:
+    torch.load = original_torch_load
+
+model.to('cpu')
+model.eval()
+
+classes = joblib.load('stl10_target_names.pkl')
+
+transform = transforms.Compose([
+    transforms.Resize((96, 96)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+
+def predict_image(image):
+    if image is None: return None
+    image_tensor = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        outputs = model(image_tensor)
+        probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
+    confidences = {classes[i]: float(probabilities[i]) for i in range(10)}
+    return confidences
+
+
+image_paths = [
+    "Screenshot 2026-01-31 150516.png",
+    "Screenshot 2026-01-31 150636.png",
+    "Screenshot 2026-01-31 150840.png",
+    "Screenshot 2026-01-31 151118.png",
+    "Screenshot 2026-01-31 151244.png"
+]
+
+example_images = [[path] for path in image_paths]
+
+def load_example_image(evt: gr.SelectData):
+    """Load the selected example image into the input component."""
+    selected_path = image_paths[evt.index]
+    return Image.open(selected_path)
+
+class_colors = ["#FF6B6B", "#4ECDC4", "#45B7D1", "#96CEB4", "#FFEAA7", 
+                "#DDA0DD", "#98D8C8", "#F7DC6F", "#BB8FCE", "#85C1E9"]
+class_badges = " ".join([
+    f'<span style="background-color: {class_colors[i]}; color: #000; padding: 4px 12px; border-radius: 15px; margin: 2px; display: inline-block; font-weight: 500;">{cls}</span>'
+    for i, cls in enumerate(classes)
+])
+
+with gr.Blocks(title="STL-10 Image Classifier") as demo:
+    gr.Markdown("# STL-10 Image Classifier")
+    gr.Markdown("""
+    Upload an image to classify it into one of the 10 STL-10 categories.
+    
+    The model is based on a CNN architecture trained on the STL-10 dataset.
+    """)
+    gr.Markdown("**Supported Classes:**")
+    gr.HTML(f'<div style="margin: 10px 0; line-height: 2.2;">{class_badges}</div>')
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Input Image")
+            submit_btn = gr.Button("Classify", variant="primary")
+            
+            gr.Markdown("### Click an example image to select it:")
+            example_gallery = gr.Gallery(
+                value=image_paths,
+                label="Examples",
+                columns=5,
+                rows=1,
+                object_fit="contain",
+                height="auto",
+                allow_preview=False
+            )
+        
+        with gr.Column():
+            label_output = gr.Label(num_top_classes=3, label="Predictions")
+
+    example_gallery.select(fn=load_example_image, inputs=None, outputs=image_input)
+    submit_btn.click(fn=predict_image, inputs=image_input, outputs=label_output)
+
+demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+gradio
+torch
+torchvision
+joblib
+Pillow

stl10_cnn_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e23e3fa28e06965880cccb4fe7bdbc7f42a72657a77370c8ebea4bedfa1615cc
+size 20470652

stl10_target_names.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:212e10f8c61b41b8bab4018b015fc353a2d2fb66f2c5147cc3444c2a101cb615
+size 91