feat: add app script

app.py

@@ -0,0 +1,104 @@
+import gradio as gr
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from PIL import Image
+from transformers import ViTForImageClassification, ViTConfig
+import random
+import numpy as np
+import transformers
+from skimage.metrics import structural_similarity as ssim
+import requests
+import os
+
+
+def set_seed(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    transformers.set_seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+
+set_seed(42)
+
+device = "cpu"
+config = ViTConfig.from_pretrained("google/vit-base-patch16-224")
+config.num_labels = 2  # Binary classification
+
+# Download the model file
+model_url = "https://huggingface.co/spuun/yummy-paws/resolve/main/best_model.pth"
+model_path = "best_model.pth"
+
+if not os.path.exists(model_path):
+    response = requests.get(model_url)
+    with open(model_path, "wb") as f:
+        f.write(response.content)
+
+# Load the trained model
+model = ViTForImageClassification.from_pretrained(
+    model_path, config=config, ignore_mismatched_sizes=True
+)
+model.classifier = nn.Linear(model.config.hidden_size, 2)
+model.to(device)
+
+# Download the reference image
+reference_image_url = (
+    "https://huggingface.co/spuun/yummy-paws/resolve/main/images%20(15).jpeg"
+)
+reference_image_path = "reference_image.jpeg"
+
+if not os.path.exists(reference_image_path):
+    response = requests.get(reference_image_url)
+    with open(reference_image_path, "wb") as f:
+        f.write(response.content)
+
+# Load the reference image for SSIM comparison
+reference_image = Image.open(reference_image_path)
+
+
+def calculate_ssim(img1, img2):
+    img1_array = np.array(img1)
+    img2_array = np.array(img2)
+    ssim_value = ssim(img1_array, img2_array, channel_axis=2)
+    return ssim_value
+
+
+def predict_and_compare(image):
+    image = image.resize(reference_image.size)
+    ssim_value = calculate_ssim(image, reference_image)
+
+    transform = transforms.Compose(
+        [
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ]
+    )
+
+    image_tensor = transform(image).unsqueeze(0).to(device)
+
+    model.eval()
+    with torch.no_grad():
+        output = model(image_tensor).logits
+        probabilities = torch.softmax(output, dim=1)[0]
+        predicted_class_index = torch.argmax(probabilities).item()
+
+        class_names = ["False", "True"]  # Assuming 0 index is False, 1 is True
+        predicted_class = class_names[predicted_class_index]
+        probability = probabilities[predicted_class_index].item()
+
+    return f"Predicted: {predicted_class}\nProbability: {probability:.4f}\nSSIM with reference: {ssim_value:.4f}"
+
+
+iface = gr.Interface(
+    fn=predict_and_compare,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Image Classification and Comparison",
+    description="Upload an image to classify it and compare with a reference image.",
+)
+
+iface.launch()