Create app.py

app.py

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+"""Streamlit demo: real vs AI-generated image classifier.
+
+Run locally with:
+    streamlit run app.py
+
+Deployed on HuggingFace Spaces — model weights are pulled from
+HF Hub on first use and cached to disk. See README.md for details.
+"""
+
+import streamlit as st
+from PIL import Image
+
+from src.predict import MODEL_REGISTRY, load_model, predict_image
+
+
+st.set_page_config(
+    page_title="Real vs AI-generated image classifier",
+    page_icon="\U0001F5BC️",
+    layout="centered",
+)
+
+st.title("Real vs AI-generated image classifier")
+st.write(
+    "Course project for DAT255 — Deep Learning Engineering. "
+    "Pick a model, upload an image, and see whether the model thinks "
+    "it's a real photograph or AI-generated."
+)
+
+
+@st.cache_resource(show_spinner="Loading model weights...")
+def _get_model(tag: str):
+    return load_model(tag, device="cpu")
+
+
+tag_by_label = {spec.display_name: tag for tag, spec in MODEL_REGISTRY.items()}
+
+chosen_label = st.selectbox(
+    "Model",
+    list(tag_by_label.keys()),
+    index=0,
+    help="Test AUC on the held-out test set is shown in the caption below.",
+)
+chosen_tag = tag_by_label[chosen_label]
+chosen_spec = MODEL_REGISTRY[chosen_tag]
+st.caption(f"Test AUC: {chosen_spec.test_auc:.4f}")
+
+uploaded = st.file_uploader(
+    "Upload an image (JPG, PNG, WebP)",
+    type=["jpg", "jpeg", "png", "webp"],
+)
+
+if uploaded is not None:
+    image = Image.open(uploaded)
+    st.image(image, caption="Your image", use_column_width=True)
+
+    model = _get_model(chosen_tag)
+    with st.spinner("Running inference..."):
+        prob_ai, label = predict_image(model, image, device="cpu")
+
+    if label == "AI-generated":
+        st.error(f"Prediction: **{label}**")
+    else:
+        st.success(f"Prediction: **{label}**")
+
+    st.write(f"Probability the image is AI-generated: **{prob_ai:.2%}**")
+    st.progress(prob_ai)
+
+    with st.expander("What does this number mean?"):
+        st.write(
+            "The model outputs a single number between 0 and 1 "
+            "(a sigmoid of its internal logit). 0 means confidently real, "
+            "1 means confidently AI-generated. The label above uses a "
+            "threshold of 0.5."
+        )
+
+st.divider()
+st.caption(
+    "Models were trained on a 60 000-image dataset split 80/10/10. "
+    "The three transfer-learning models fine-tune ImageNet-pretrained "
+    "backbones; the scratch ResNet-50 was trained from random "
+    "initialisation with ReLU replaced by GELU throughout the network."
+)