Upload app.py

app.py

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+import torch
+import torchvision
+import torchvision.transforms as transforms
+import torch.nn.functional as F
+from PIL import Image
+import streamlit as st
+import requests
+from io import BytesIO
+
+# Page config
+st.set_page_config(page_title="Adversarial Self-Driving Test", layout="centered")
+
+# Title
+st.title("💥 Adversarial Self-Driving Car Tester")
+st.markdown("Upload a traffic sign, and we'll **confuse the AI model** into causing a virtual accident!")
+
+# Load model + labels
+model = torchvision.models.resnet18(pretrained=True)
+model.eval()
+
+LABELS_URL = "https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"
+labels = requests.get(LABELS_URL).text.strip().split("\n")
+
+# Transform
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+])
+
+# Choose image source
+use_default = st.checkbox("🚦 Use Default Stop Sign")
+if use_default:
+    img_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/6/6b/Stop_sign_light_red.svg/512px-Stop_sign_light_red.svg.png"
+    image = Image.open(BytesIO(requests.get(img_url).content)).convert('RGB')
+    st.image(image, caption="Stop Sign", use_column_width=True)
+else:
+    uploaded_file = st.file_uploader("📷 Upload a traffic sign image", type=["jpg", "jpeg", "png"])
+    if uploaded_file:
+        image = Image.open(uploaded_file).convert('RGB')
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+    else:
+        image = None
+
+# Epsilon
+epsilon = st.slider("⚡ Perturbation Strength (epsilon)", 0.001, 0.1, 0.01, step=0.001)
+
+# Target class selector
+target_class = st.selectbox(
+    "🎯 Confuse the model into predicting:",
+    options=[
+        (919, "Stop Sign"),
+        (717, "Speed Limit 60"),
+        (718, "Speed Limit 80"),
+        (400, "Speedboat (LOL why?)"),
+    ],
+    format_func=lambda x: f"{x[0]} - {x[1]}"
+)
+target_class_id = target_class[0]
+target_class_label = target_class[1]
+
+# Process
+if image:
+    input_tensor = transform(image).unsqueeze(0)
+    input_tensor.requires_grad = True
+
+    # Original prediction
+    with torch.no_grad():
+        orig_out = model(input_tensor)
+        orig_pred_idx = orig_out.argmax().item()
+        orig_pred = labels[orig_pred_idx]
+    st.markdown(f"✅ **Original Prediction:** `{orig_pred}`")
+
+    # FGSM Attack
+    output = model(input_tensor)
+    loss = F.cross_entropy(output, torch.tensor([target_class_id]))
+    loss.backward()
+    perturb = epsilon * input_tensor.grad.sign()
+    adv_tensor = torch.clamp(input_tensor + perturb, 0, 1)
+
+    # Adversarial prediction
+    with torch.no_grad():
+        adv_out = model(adv_tensor)
+        adv_pred_idx = adv_out.argmax().item()
+        adv_pred = labels[adv_pred_idx]
+    st.markdown(f"⚠️ **Adversarial Prediction:** `{adv_pred}`")
+
+    # Show adversarial image
+    st.image(transforms.ToPILImage()(adv_tensor.squeeze()), caption="Adversarial Image", use_column_width=True)
+
+    # Accident message
+    if orig_pred != adv_pred:
+        st.markdown("#### 🚨 Accident Report")
+        st.error(f"🤯 The car thought a `{orig_pred}` was a `{adv_pred}`. That's a full-on self-driving fail!")
+        st.markdown("😬 *Hope nobody was crossing the road...*")
+        st.markdown("💀 *Insurance premium just went up!*")
+        st.markdown("🧠 *Back to AI driving school, buddy.*")