Upload app.py

app.py

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+
+import os
+import gradio as gr
+import torch
+import numpy as np
+import cv2
+from PIL import Image
+from transformers import ViTImageProcessor, ViTForImageClassification
+# from transformers import AutoModelForImageClassification, AutoImageProcessor
+
+# -----------------------------
+# CONFIGURATION
+# -----------------------------
+
+MODEL_REPO = "SARVM/ViT_Deepfake"
+# HF_TOKEN = os.getenv("HF_TOKEN")  # Set in Space secrets or local env
+HF_TOKEN = "hf_xxxxxxxxxxxxxxxxxxxxxxxx"  # 🔐 Replace with your actual Hugging Face token
+
+print(f"Loading model from {MODEL_REPO}...")
+
+processor = ViTImageProcessor.from_pretrained(
+    MODEL_REPO,
+    token=HF_TOKEN
+)
+
+model = ViTForImageClassification.from_pretrained(
+    MODEL_REPO,
+    token=HF_TOKEN,
+    output_attentions=True
+)
+
+# processor = AutoImageProcessor.from_pretrained(
+#     MODEL_REPO,
+#     token=HF_TOKEN
+# )
+
+# model = AutoModelForImageClassification.from_pretrained(
+#     MODEL_REPO,
+#     token=HF_TOKEN
+# )
+
+model.eval()
+
+# Override labels to REAL / FAKE
+model.config.id2label = {
+    1: "REAL",
+    0: "FAKE"
+}
+
+model.config.label2id = {
+    "REAL": 1,
+    "FAKE": 0
+}
+
+# -----------------------------
+# ATTENTION ROLLOUT
+# -----------------------------
+
+def compute_attention_rollout(attentions):
+    att_mat = torch.stack(attentions).squeeze(1)
+    att_mat = att_mat.mean(dim=1)
+
+    residual_att = torch.eye(att_mat.size(-1))
+    aug_att_mat = att_mat + residual_att
+    aug_att_mat = aug_att_mat / aug_att_mat.sum(dim=-1).unsqueeze(-1)
+
+    joint_attentions = torch.zeros_like(aug_att_mat)
+    joint_attentions[0] = aug_att_mat[0]
+
+    for n in range(1, aug_att_mat.size(0)):
+        joint_attentions[n] = aug_att_mat[n] @ joint_attentions[n - 1]
+
+    return joint_attentions[-1]
+
+
+# -----------------------------
+# PREDICTION FUNCTION
+# -----------------------------
+
+def predict(image):
+    if image is None:
+        return None, None, None
+
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs, output_attentions=True)
+        logits = outputs.logits
+        attentions = outputs.attentions
+
+    probs = torch.nn.functional.softmax(logits, dim=-1)
+    confidence, predicted_class_idx = torch.max(probs, dim=-1)
+
+    prediction = model.config.id2label[predicted_class_idx.item()]
+    confidence_pct = round(confidence.item() * 100, 2)
+
+    # Attention rollout
+    rollout = compute_attention_rollout(attentions)
+
+    mask = rollout[0, 1:]
+    size = int(mask.shape[0] ** 0.5)
+    mask = mask.reshape(size, size).cpu().numpy()
+
+    mask = cv2.resize(mask, image.size)
+    mask = (mask - mask.min()) / (mask.max() - mask.min() + 1e-8)
+
+    heatmap = cv2.applyColorMap(
+        np.uint8(255 * mask),
+        cv2.COLORMAP_JET
+    )
+
+    overlay = cv2.addWeighted(
+        np.array(image),
+        0.6,
+        heatmap,
+        0.4,
+        0
+    )
+
+    return prediction, f"{confidence_pct}%", overlay
+
+
+# -----------------------------
+# UI DESIGN
+# -----------------------------
+
+custom_css = """
+/* Professional Adaptive Theme */
+:root {
+    --primary-blue: #2563eb;
+    --hero-text: #0f172a; /* Dark slate for light mode */
+}
+
+.dark {
+    --hero-text: #f8fafc; /* White for dark mode */
+}
+
+/* Background refinement */
+body {
+    background-color: var(--background-fill-primary);
+}
+
+/* Adaptive Typography */
+.hero {
+    text-align: center;
+    font-family: 'Inter', sans-serif;
+    font-size: 48px;
+    font-weight: 800;
+    letter-spacing: -0.04em;
+    margin-top: 50px;
+    /* This variable handles the visibility toggle */
+    color: var(--hero-text) !important;
+}
+
+.sub {
+    text-align: center;
+    opacity: 0.7;
+    font-size: 14px;
+    font-weight: 600;
+    letter-spacing: 0.1em;
+    text-transform: uppercase;
+    margin-bottom: 40px;
+    color: var(--body-text-color);
+}
+
+/* Professional Container Styling */
+.glass {
+    background: var(--block-background-fill) !important;
+    border: 1px solid var(--border-color-primary) !important;
+    border-radius: 12px !important;
+    padding: 24px !important;
+    box-shadow: var(--block-shadow);
+    transition: all 0.2s ease;
+}
+
+.glass:hover {
+    border-color: var(--primary-blue) !important;
+    box-shadow: 0 4px 20px rgba(37, 99, 235, 0.1);
+}
+
+/* Enterprise Button */
+button.primary {
+    background: var(--primary-blue) !important;
+    color: white !important;
+    border: none !important;
+    font-weight: 600 !important;
+    padding: 12px 24px !important;
+    border-radius: 8px !important;
+    box-shadow: 0 4px 12px rgba(37, 99, 235, 0.2) !important;
+}
+
+button.primary:hover {
+    background: #1d4ed8 !important;
+    transform: translateY(-1px);
+    box-shadow: 0 6px 16px rgba(37, 99, 235, 0.3) !important;
+}
+
+/* Label & Input tweaks for clarity */
+.gr-label {
+    font-weight: 600 !important;
+    font-size: 12px !important;
+    text-transform: uppercase;
+    color: var(--primary-blue) !important;
+}
+"""
+
+with gr.Blocks(
+    css=custom_css,
+    theme=gr.themes.Soft(
+        primary_hue="blue", 
+        font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui"]
+    )
+) as demo:
+
+    gr.Markdown(f"<div class='hero'>FORESIGHT<span style='color:#3b82f6'>.</span></div>")
+    gr.Markdown("<div class='sub'>Deep Intelligence Neural Analysis</div>")
+
+    with gr.Row():
+        with gr.Column():
+            with gr.Group(elem_classes="glass"):
+                input_image = gr.Image(type="pil", label="Source Input")
+                run_btn = gr.Button("RUN DIAGNOSTIC", variant="primary")
+
+        with gr.Column():
+            with gr.Group(elem_classes="glass"):
+                output_label = gr.Label(label="Classification Verdict")
+                output_conf = gr.Textbox(label="Confidence Rating", interactive=False)
+                heatmap_output = gr.Image(label="Vulnerability Visualization")
+
+    run_btn.click(
+        fn=predict,
+        inputs=input_image,
+        outputs=[output_label, output_conf, heatmap_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()