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from __future__ import annotations
import hashlib
import json
import logging
import math
import os
from pathlib import Path
import sys
import threading
import time
# Gradio probes its own local HTTP server during startup. Keep local traffic
# direct even on machines configured with outbound HTTP proxies.
for key in ("NO_PROXY", "no_proxy"):
existing = os.environ.get(key, "")
os.environ[key] = ",".join(
filter(None, [existing, "localhost", "127.0.0.1", "::1"])
)
import gradio as gr
from huggingface_hub import snapshot_download
from PIL import Image, UnidentifiedImageError
import torch
sys.path.insert(0, str(Path(__file__).absolute().parent))
from attention import (
capture_prediction,
draw_grid,
head_view,
parameter_rows,
patch_view,
public_attention,
tile_boxes,
tile_overlay,
top_tiles,
)
from presentation import (
EMPTY_RESULT,
HEADER,
patch_matrix,
result_card,
stats_card,
weight_chart,
)
from sample_images import (
gallery_items,
sample_at,
annotate_example,
same_image_as_state,
)
from locale_settings import english_app_kwargs
LOG = logging.getLogger(__name__)
MAX_PIXELS = int(os.environ.get("LTF_MAX_PIXELS", "16000000"))
MAX_VIEWS = int(os.environ.get("LTF_MAX_VIEWS", "400"))
MODEL_LOCK = threading.Lock()
_detector = None
_model_revision = None
def validate_upload(path):
if not path:
raise gr.Error("Please upload an image first.")
source = Path(path)
try:
with Image.open(source) as image:
width, height = image.size
if getattr(image, "n_frames", 1) != 1:
raise gr.Error("Please upload a single-frame image.")
views = math.ceil(width / 224) * math.ceil(height / 224) + 1
if width * height > MAX_PIXELS or views > MAX_VIEWS:
raise gr.Error(
f"Image exceeds demo limits: {MAX_PIXELS:,} pixels "
f"and {MAX_VIEWS} views. Please choose a smaller original image."
)
image.verify()
except (UnidentifiedImageError, OSError, ValueError, Image.DecompressionBombError):
raise gr.Error("This file is not a readable image.") from None
return source, width, height, views
def resolve_model_dir():
local_dir = os.environ.get("LTF_MODEL_DIR")
if local_dir:
root = Path(local_dir).expanduser().resolve()
if not root.is_dir():
raise RuntimeError("LTF_MODEL_DIR does not point to a model directory.")
return root
repo = os.environ.get("LTF_MODEL_REPO")
if not repo:
raise RuntimeError("Set LTF_MODEL_REPO or LTF_MODEL_DIR before inference.")
return Path(
snapshot_download(
repo_id=repo,
revision=os.environ.get("LTF_MODEL_REVISION", "main"),
allow_patterns=[
"*.json",
"configs/*",
"weights/*",
"backbones/*",
"ltf_final/*.py",
"model/*.py",
"mil_module/*.py",
"utils/*.py",
],
)
)
def verify_model(root):
manifest = json.loads((root / "release_manifest.json").read_text())
for relative, expected in manifest["files"].items():
relative_path = Path(relative)
# Hub snapshot files are symlinks into the sibling blobs directory.
# Validate the manifest path before following links, then hash the bytes.
if relative_path.is_absolute() or ".." in relative_path.parts:
raise RuntimeError("The model manifest contains an invalid path.")
path = root / relative_path
if not path.is_file():
raise RuntimeError("The model release is incomplete.")
digest = hashlib.sha256()
with path.open("rb") as stream:
for block in iter(lambda: stream.read(8 * 1024 * 1024), b""):
digest.update(block)
if digest.hexdigest() != expected["sha256"]:
raise RuntimeError(f"Model release checksum failed: {relative}")
return manifest
def get_detector():
"""Called under MODEL_LOCK, so initialization and inference are serialized."""
global _detector, _model_revision
if _detector is None:
root = resolve_model_dir()
verify_model(root)
sys.path.insert(0, str(root))
from ltf_final import LTFDetector
torch.set_num_threads(int(os.environ.get("LTF_CPU_THREADS", "2")))
_detector = LTFDetector(
config=root / "configs/eval.yaml",
weights=root / "weights/epoch_20.pth",
device=os.environ.get("LTF_DEVICE", "auto"),
chunk_size=int(os.environ.get("LTF_CHUNK_SIZE", "8")),
)
_model_revision = os.environ.get("LTF_MODEL_REVISION", "local")
return _detector
def _predict_request(image_path, branch="fusion", threshold=0.5, with_attention=False):
source, width, height, views = validate_upload(image_path)
if branch not in ("fusion", "level1", "level2"):
raise gr.Error("Unknown model branch.")
if not math.isfinite(float(threshold)) or not 0 <= float(threshold) <= 1:
raise gr.Error("Threshold must be between 0 and 1.")
start = time.perf_counter()
try:
with MODEL_LOCK:
detector = get_detector()
if with_attention:
result, attention = capture_prediction(
detector, source, branch, float(threshold)
)
state = dict(
path=str(source),
attention=attention,
boxes=tile_boxes(
width, height, int(detector.model.input_resolution)
),
parameters=parameter_rows(
detector, branch, float(threshold), views
),
)
else:
result = detector.predict(source, branch, float(threshold))
state = None
except torch.cuda.OutOfMemoryError:
torch.cuda.empty_cache()
raise gr.Error(
"GPU memory is insufficient. Please try a smaller image."
) from None
except gr.Error:
raise
except Exception:
LOG.exception("LTF inference failed")
raise gr.Error(
"Model inference failed. Check the Space runtime logs."
) from None
# The preview never replaces the original image passed to the model.
with Image.open(source) as original:
preview = original.convert("RGB")
preview.thumbnail((1024, 1024))
result.pop("path", None)
result.update(
width=width,
height=height,
model_revision=_model_revision,
elapsed_seconds=round(time.perf_counter() - start, 3),
)
assert result["views"] == views, "Preview and model tile counts disagree"
scores = result["probabilities"][branch]
summary = result_card(result)
rows = [
[
name,
result["probabilities"][name]["real"],
result["probabilities"][name]["fake"],
]
for name in ("fusion", "level1", "level2")
]
if state:
result["attention"] = public_attention(state)
state["result"] = dict(result)
return (
preview,
summary,
{"Real": scores["real"], "AI-generated": scores["fake"]},
rows,
result,
), state
def predict(image_path, branch="fusion", threshold=0.5):
"""Keep the original five-output public prediction API compatible."""
return _predict_request(image_path, branch, threshold)[0]
def explain(image_path, branch="fusion", threshold=0.5):
base, state = _predict_request(image_path, branch, threshold, with_attention=True)
attention = state["attention"]
count = len(state["boxes"])
if len(attention["l1"]) != count or len(attention["l2"]) != count + 1:
raise gr.Error("Attention and original image tile counts disagree.")
grid = draw_grid(state["path"], state["boxes"])
l1_image, l1_description = head_view(state, "mean")
l2_image = tile_overlay(
state["path"], state["boxes"], attention["l2"][1:], reference_count=count + 1
)
description = (
f'Global view weight **{attention["l2"][0]*100:.2f}%**; total local view weight '
f'**{float(attention["l2"][1:].sum())*100:.2f}%**. '
'The global view is listed separately; the overlay shows local tiles only.\n\n'
f'Feature fusion coefficients: L1 脳 {attention["gate"]:.4f}, L2 脳 {1-attention["gate"]:.4f}. '
'These coefficients scale branch embeddings, not branch probabilities.'
)
weight_rows = [["Global", "whole image", None, float(attention["l2"][0])]]
weight_rows.extend(
[
f"T{i + 1}",
str(list(box)),
float(attention["l1"][i]),
float(attention["l2"][i + 1]),
]
for i, box in enumerate(state["boxes"])
)
selected = int(attention["l1"].argmax()) + 1
tile, patch, patch_description = patch_view(state, str(selected))
return (
*base,
grid,
l1_image,
l1_description,
l2_image,
description,
top_tiles(state),
state["parameters"],
weight_rows,
state,
gr.Dropdown(choices=[str(i + 1) for i in range(count)], value=str(selected)),
tile,
patch,
patch_description,
gr.Dropdown(
choices=[("Mean (8 Heads)", "mean")]
+ [
(f"Head {i + 1}", f"head_{i + 1}")
for i in range(attention["l1_heads"].shape[0])
],
value="mean",
),
weight_chart(state),
weight_chart(state, level="l2"),
patch_matrix(state, str(selected)),
stats_card(state),
gr.update(visible=True),
)
def inspect_head(state, selection):
image, description = head_view(state, selection)
return image, description, weight_chart(state, selection)
def inspect_patch(state, tile_number):
tile, image, description = patch_view(state, tile_number)
return tile, image, description, patch_matrix(state, tile_number)
def select_top_tile(state, event: gr.SelectData):
if not state:
return gr.skip()
index = event.index[0] if isinstance(event.index, (tuple, list)) else event.index
order = sorted(
range(len(state["boxes"])), key=lambda i: -float(state["attention"]["l1"][i])
)[:4]
return str(order[int(index)] + 1)
def update_decision(state, branch, threshold):
"""Threshold and branch selection use cached logits, without another forward."""
if not state:
return tuple(gr.skip() for _ in range(5))
result = dict(state["result"])
score = result["probabilities"][branch]["fake"]
label = int(score >= float(threshold))
result.update(
branch=branch,
threshold=float(threshold),
score_fake=score,
label=label,
prediction="fake" if label else "real",
)
scores = result["probabilities"][branch]
rows = [
[
name,
result["probabilities"][name]["real"],
result["probabilities"][name]["fake"],
]
for name in ("fusion", "level1", "level2")
]
parameters = [list(row) for row in state["parameters"]]
parameters[0][2] = f"{branch} / {float(threshold):.2f}"
return (
result_card(result),
{"Real": scores["real"], "AI-generated": scores["fake"]},
rows,
result,
parameters,
)
def reset_upload(path):
image = None
if path:
try:
source, *_ = validate_upload(path)
with Image.open(source) as original:
image = original.convert("RGB")
image.thumbnail((1024, 1024))
except gr.Error:
pass
return image, EMPTY_RESULT, None, [], None, [], None, gr.update(visible=False)
def run_example(branch, threshold, event: gr.SelectData):
sample, path = sample_at(event.index)
outputs = explain(str(path), branch, threshold)
description = annotate_example(outputs, sample)
return (*outputs, str(path), description)
def on_upload_change(path, current_state):
if same_image_as_state(path, current_state):
return tuple(gr.skip() for _ in range(9))
return (*reset_upload(path), "")
def build_demo():
with gr.Blocks(
title="LTF 路 Image Detection and Attention Analysis",
fill_width=True,
delete_cache=(3600, 3600),
) as demo:
gr.HTML(HEADER)
with gr.Row():
with gr.Column(scale=3, min_width=260, elem_id="upload-panel"):
gr.Markdown(
"### 01 路 Upload an image\nInference uses the original file. Resizing the preview does not change the model input."
)
upload = gr.File(
label="Image file",
type="filepath",
file_count="single",
file_types=["image"],
height=150,
)
clear_upload = gr.ClearButton([upload], value="Change image", size="sm")
branch = gr.Dropdown(
[
("Fusion 路 Combined branches", "fusion"),
("L1 路 Patch-level branch", "level1"),
("L2 路 View-level branch", "level2"),
],
value="fusion",
label="Prediction branch",
elem_id="branch-select",
)
threshold = gr.Slider(
0,
1,
value=0.5,
step=0.01,
label="Decision threshold",
elem_id="threshold-select",
)
button = gr.Button(
"Run Analysis", variant="primary", elem_id="run-button"
)
gr.Markdown(
f"Limits: {MAX_PIXELS/1_000_000:g} MP 路 {MAX_VIEWS} views 路 30 MB. Large images take longer on CPU."
)
with gr.Column(scale=4, min_width=300):
preview = gr.Image(
label="Original image preview",
height=390,
interactive=False,
buttons=["download", "fullscreen"],
elem_id="original-preview",
)
with gr.Column(scale=4, min_width=300):
summary = gr.HTML(EMPTY_RESULT, label="Analysis result")
scores = gr.Label(
label="Model scores", num_top_classes=2, visible=False
)
with gr.Column(elem_id="example-section"):
gr.Markdown(
"### GenImage examples 路 Click an image to analyze\nReal / Generated captions are dataset labels. Model predictions and attention appear in the analysis panels."
)
example_gallery = gr.Gallery(
value=gallery_items(),
columns=4,
rows=2,
height="auto",
label="GenImage validation examples",
show_label=False,
object_fit="contain",
allow_preview=False,
preview=False,
interactive=False,
buttons=[],
elem_id="example-gallery",
)
example_info = gr.Markdown("", elem_id="example-info")
state = gr.State(value=None, time_to_live=3600)
with gr.Column(visible=False, elem_id="analysis-section") as analysis:
statistics = gr.HTML()
gr.Markdown(
"## 02 路 Attention Analysis\nAll heatmaps use the same uniform-attention reference. Nearly uniform weights have similar colors. Attention is not a forgery probability."
)
with gr.Tabs(selected="l1", elem_id="analysis-tabs"):
with gr.Tab("L1 路 Tile Attention", id="l1"):
with gr.Row():
with gr.Column(scale=7, min_width=320):
l1_image = gr.Image(
label="L1 tile attention overlay",
height=500,
interactive=False,
buttons=["download", "fullscreen"],
elem_classes="attention-image",
)
l1_description = gr.Markdown()
with gr.Column(scale=5, min_width=300):
head = gr.Dropdown(
[("Mean (8 Heads)", "mean")],
value="mean",
label="Aggregation head",
elem_id="head-select",
)
l1_chart = gr.HTML()
with gr.Tab("L2 路 View Weights", id="l2"):
with gr.Row():
with gr.Column(scale=7, min_width=320):
l2_image = gr.Image(
label="L2 local view attention overlay",
height=500,
interactive=False,
buttons=["download", "fullscreen"],
elem_classes="attention-image",
)
attention_description = gr.Markdown()
with gr.Column(scale=5, min_width=300):
l2_chart = gr.HTML()
with gr.Tab("Original 路 Tile Layout", id="grid"):
grid = gr.Image(
label="Original 224 脳 224 tile boundaries",
height=570,
interactive=False,
buttons=["download", "fullscreen"],
elem_classes="attention-image",
)
gr.Markdown(
"Tiles are numbered row by row. Full 224-pixel blocks do not overlap; only the final right/bottom boundary blocks may overlap. Some labels are omitted on dense grids; complete coordinates are listed below."
)
with gr.Tab("Tile 路 Patch Details", id="patch"):
gr.Markdown(
"### Top 4 tiles by L1 attention\nClick a tile to inspect its original crop, attention overlay, and patch weights. The top-four selection is for display only and does not affect prediction."
)
gallery = gr.Gallery(
label="High-attention tiles",
columns=4,
object_fit="contain",
height=250,
elem_id="top-crops",
show_label=False,
preview=False,
allow_preview=False,
interactive=False,
buttons=["download_all"],
)
tile_number = gr.Dropdown(
choices=[],
label="Tile number",
allow_custom_value=False,
elem_id="tile-select",
)
with gr.Row():
with gr.Column(min_width=250):
selected_tile = gr.Image(
label="Original 224 脳 224 crop",
height=330,
interactive=False,
buttons=["download", "fullscreen"],
)
with gr.Column(min_width=250):
selected_patch = gr.Image(
label="Patch attention overlay",
height=330,
interactive=False,
buttons=["download", "fullscreen"],
)
with gr.Column(min_width=280):
patch_chart = gr.HTML()
patch_description = gr.Markdown()
with gr.Accordion(
"Exact weights, parameters, and raw output",
open=False,
elem_id="technical-details",
):
gr.Markdown("### Branch scores")
table = gr.Dataframe(
headers=["Branch", "Real score", "Generated score"],
datatype=["str", "number", "number"],
interactive=False,
)
gr.Markdown(
"### View weights\nL1 is the mean across eight heads. The first L2 entry represents the separate global view."
)
weight_table = gr.Dataframe(
headers=["View", "Original crop box", "L1 mean weight", "L2 weight"],
datatype=["str", "str", "number", "number"],
interactive=False,
)
gr.Markdown(
"### Model and hyperparameters\nTraining-only rows record the historical configuration. Random augmentation, Cross-Shuffle, and dropout are disabled during inference."
)
parameters = gr.Dataframe(
headers=["Scope", "Parameter", "Value"],
datatype=["str", "str", "str"],
interactive=False,
)
details = gr.JSON(label="Prediction JSON")
analysis_outputs = [
preview,
summary,
scores,
table,
details,
grid,
l1_image,
l1_description,
l2_image,
attention_description,
gallery,
parameters,
weight_table,
state,
tile_number,
selected_tile,
selected_patch,
patch_description,
head,
l1_chart,
l2_chart,
patch_chart,
statistics,
analysis,
]
example_event = example_gallery.select(
run_example,
[branch, threshold],
analysis_outputs + [upload, example_info],
api_name=False,
concurrency_limit=1,
concurrency_id="ltf-inference",
trigger_mode="always_last",
scroll_to_output=True,
)
run_event = button.click(
explain,
[upload, branch, threshold],
analysis_outputs,
api_name="explain",
concurrency_limit=1,
concurrency_id="ltf-inference",
cancels=[example_event],
)
example_gallery.select(fn=None, cancels=[run_event], queue=False)
upload.upload(fn=None, cancels=[example_event], queue=False)
clear_upload.click(fn=None, cancels=[example_event], queue=False)
head.change(
inspect_head,
[state, head],
[l1_image, l1_description, l1_chart],
api_name=False,
)
tile_number.change(
inspect_patch,
[state, tile_number],
[selected_tile, selected_patch, patch_description, patch_chart],
api_name=False,
)
gallery.select(select_top_tile, [state], [tile_number], api_name=False)
for control in (branch, threshold):
control.change(
update_decision,
[state, branch, threshold],
[summary, scores, table, details, parameters],
api_name=False,
queue=False,
trigger_mode="always_last",
)
upload.change(
on_upload_change,
[upload, state],
[
preview,
summary,
scores,
table,
details,
parameters,
state,
analysis,
example_info,
],
api_name=False,
queue=False,
cancels=[run_event],
)
api_button = gr.Button(visible=False)
api_button.click(
predict,
[upload, branch, threshold],
[preview, summary, scores, table, details],
api_name="predict",
concurrency_limit=1,
concurrency_id="ltf-inference",
)
gr.Markdown(
"LTF 路 Original checkpoint and inference pipeline 路 Visualization preserves model scores",
elem_id="footer-note",
)
return demo.queue(max_size=16, default_concurrency_limit=1)
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
build_demo().launch(
server_name=os.environ.get("GRADIO_SERVER_NAME", "0.0.0.0"),
server_port=int(os.environ.get("PORT", "7860")),
share=False,
max_file_size="30mb",
app_kwargs=english_app_kwargs(),
ssr_mode=False,
footer_links=["api"],
theme=gr.themes.Soft(
primary_hue="teal",
neutral_hue="slate",
font=["ui-sans-serif", "system-ui", "sans-serif"],
).set(block_label_background_fill="#eef3f8", block_label_text_color="#445b72"),
css_paths=Path(__file__).with_name("styles.css"),
)
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