Upload app (1).py

app (1).py

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+"""
+DeepFake Detector — HuggingFace Gradio Space
+=============================================
+Architecture : ConvNeXt-Base + custom classifier head
+Weights      : ARPAN2026/dfake-hcnext  (auto-downloaded on first run)
+Classes      : Real (0)  |  Fake (1)
+"""
+
+import os
+import urllib.request
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+import timm
+import gradio as gr
+from PIL import Image
+
+
+# ============================================================
+# CONFIG
+# ============================================================
+MODEL_URL  = "https://huggingface.co/ARPAN2026/dfake-hcnext/resolve/main/best_model_New.pth"
+MODEL_PATH = "best_model_New.pth"
+IMG_SIZE   = 224
+DEVICE     = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Image normalisation (same as training pipeline)
+TRANSFORM = transforms.Compose([
+    transforms.Resize((IMG_SIZE, IMG_SIZE)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.5] * 3, [0.5] * 3),
+])
+
+
+# ============================================================
+# MODEL DEFINITION
+# ============================================================
+class DeepfakeModel(nn.Module):
+    """ConvNeXt-Base backbone with a lightweight two-class classifier head."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.backbone = timm.create_model(
+            "convnext_base", pretrained=False, num_classes=0
+        )
+        dim = self.backbone.num_features
+        self.classifier = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, 256),
+            nn.GELU(),
+            nn.Dropout(0.4),
+            nn.Linear(256, 2),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        features = self.backbone.forward_features(x)
+        if features.ndim == 4:                       # (B, C, H, W) → (B, C)
+            features = features.flatten(2).mean(-1)
+        return self.classifier(features)
+
+
+# ============================================================
+# UTILITIES
+# ============================================================
+def download_weights() -> None:
+    """Download model weights from HuggingFace Hub if not already present."""
+    if not os.path.exists(MODEL_PATH):
+        print(f"[INFO] Downloading weights from:\n       {MODEL_URL}")
+        urllib.request.urlretrieve(MODEL_URL, MODEL_PATH)
+        print("[INFO] Download complete.")
+    else:
+        print(f"[INFO] Weights already found at '{MODEL_PATH}' — skipping download.")
+
+
+def load_model() -> DeepfakeModel:
+    """Instantiate DeepfakeModel, load saved weights, and set to eval mode."""
+    net = DeepfakeModel().to(DEVICE)
+    state_dict = torch.load(MODEL_PATH, map_location=DEVICE)
+    net.load_state_dict(state_dict)
+    net.eval()
+    print(f"[INFO] Model ready on {DEVICE}.")
+    return net
+
+
+# ============================================================
+# INFERENCE
+# ============================================================
+def predict(model: DeepfakeModel, image: Image.Image):
+    """
+    Run inference on a single PIL image.
+
+    Parameters
+    ----------
+    model : DeepfakeModel
+        Loaded, eval-mode model.
+    image : PIL.Image.Image | None
+        Image uploaded by the user.
+
+    Returns
+    -------
+    label_dict : dict[str, float]
+        Mapping of class name → probability (consumed by gr.Label).
+    verdict_md : str
+        Markdown-formatted verdict string.
+    """
+    if image is None:
+        return {"Error": 1.0}, "⚠️ Please upload an image first."
+
+    tensor = TRANSFORM(image.convert("RGB")).unsqueeze(0).to(DEVICE)
+
+    with torch.no_grad():
+        logits = model(tensor)
+        probs  = torch.softmax(logits, dim=1).cpu().numpy()[0]
+
+    real_prob  = float(probs[0])
+    fake_prob  = float(probs[1])
+    confidence = max(real_prob, fake_prob) * 100
+
+    if fake_prob > real_prob:
+        verdict_md = f"## 🔴 DEEPFAKE DETECTED\n**Confidence:** {confidence:.1f}%"
+    else:
+        verdict_md = f"## 🟢 LIKELY REAL\n**Confidence:** {confidence:.1f}%"
+
+    label_dict = {
+        "Real": round(real_prob, 4),
+        "Fake": round(fake_prob, 4),
+    }
+    return label_dict, verdict_md
+
+
+# ============================================================
+# CUSTOM CSS  (dark forensic theme)
+# ============================================================
+CSS = """
+@import url('https://fonts.googleapis.com/css2?family=Share+Tech+Mono&family=Syne:wght@400;700;800&display=swap');
+
+:root {
+    --bg:      #0a0c10;
+    --surface: #111318;
+    --border:  #1e2330;
+    --accent:  #00e5ff;
+    --danger:  #ff3b5c;
+    --safe:    #00e676;
+    --text:    #d0d8f0;
+    --muted:   #5a6480;
+    --radius:  8px;
+}
+
+body, .gradio-container {
+    background: var(--bg) !important;
+    font-family: 'Syne', sans-serif !important;
+    color: var(--text) !important;
+}
+
+h1.title-heading {
+    font-family: 'Syne', sans-serif;
+    font-weight: 800;
+    font-size: 2.4rem;
+    letter-spacing: -0.02em;
+    background: linear-gradient(90deg, var(--accent), #7b61ff);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    margin: 0;
+}
+
+p.subtitle {
+    color: var(--muted);
+    font-family: 'Share Tech Mono', monospace;
+    font-size: 0.85rem;
+    margin-top: 4px;
+    letter-spacing: 0.08em;
+}
+
+.gr-box, .gr-panel, .gr-form {
+    background: var(--surface) !important;
+    border: 1px solid var(--border) !important;
+    border-radius: var(--radius) !important;
+}
+
+.gr-image, .svelte-1n8nu59 {
+    border: 2px dashed var(--border) !important;
+    border-radius: var(--radius) !important;
+    background: #0d0f14 !important;
+}
+
+button.primary {
+    background: var(--accent) !important;
+    color: #000 !important;
+    font-family: 'Syne', sans-serif !important;
+    font-weight: 700 !important;
+    border: none !important;
+    border-radius: var(--radius) !important;
+    letter-spacing: 0.05em;
+}
+
+button.secondary {
+    background: transparent !important;
+    border: 1px solid var(--border) !important;
+    color: var(--muted) !important;
+    font-family: 'Syne', sans-serif !important;
+    border-radius: var(--radius) !important;
+}
+
+.gr-markdown h2 {
+    font-family: 'Syne', sans-serif;
+    font-size: 1.4rem;
+    font-weight: 700;
+    margin: 0 0 4px;
+}
+
+.gr-label .wrap {
+    background: var(--surface) !important;
+    border: 1px solid var(--border) !important;
+    border-radius: var(--radius) !important;
+}
+
+.gr-label .label-wrap span {
+    font-family: 'Share Tech Mono', monospace !important;
+    color: var(--text) !important;
+}
+
+.gr-label .bar {
+    background: linear-gradient(90deg, var(--accent), #7b61ff) !important;
+}
+
+footer { display: none !important; }
+"""
+
+
+# ============================================================
+# GRADIO UI BUILDER
+# ============================================================
+def build_ui(model: DeepfakeModel) -> gr.Blocks:
+    """
+    Construct and return the Gradio Blocks interface.
+
+    Parameters
+    ----------
+    model : DeepfakeModel
+        Pre-loaded, eval-mode model passed in via closure.
+
+    Returns
+    -------
+    gr.Blocks
+        The assembled Gradio app (not yet launched).
+    """
+
+    def _predict_wrapper(image: Image.Image):
+        """Closure wrapper — captures `model` from the outer scope."""
+        return predict(model, image)
+
+    with gr.Blocks(css=CSS, title="DeepFake Detector") as demo:
+
+        # ── Header ──────────────────────────────────────────────────
+        gr.HTML("""
+        <div style="text-align:center; padding:32px 0 16px;">
+            <h1 class='title-heading'>DEEPFAKE DETECTOR</h1>
+            <p class='subtitle'>
+                ConvNeXt-Base &nbsp;·&nbsp; Trained on RVF Faces &nbsp;·&nbsp; Hackathon Edition
+            </p>
+        </div>
+        """)
+
+        # ── Main two-column layout ───────────────────────────────────
+        with gr.Row():
+
+            # Left column — upload + controls + model info
+            with gr.Column(scale=1):
+                image_input = gr.Image(
+                    type="pil",
+                    label="Upload Face Image",
+                    height=320,
+                )
+
+                with gr.Row():
+                    submit_btn = gr.Button("🔍  Analyze", variant="primary")
+                    clear_btn  = gr.ClearButton(
+                        components=[image_input],
+                        value="✕  Clear",
+                    )
+
+                gr.HTML("""
+                <div style="margin-top:12px; padding:12px 16px;
+                            background:#0d0f14; border:1px solid #1e2330;
+                            border-radius:8px; font-family:'Share Tech Mono',monospace;
+                            font-size:0.78rem; color:#5a6480; line-height:1.9;">
+                    <b style="color:#00e5ff;">MODEL</b> &nbsp;&nbsp; ConvNeXt-Base + custom head<br>
+                    <b style="color:#00e5ff;">TRAINED</b>&nbsp; Real vs Fake Faces (80/20 split)<br>
+                    <b style="color:#00e5ff;">INPUT</b> &nbsp;&nbsp; 224 × 224 · RGB · normalised<br>
+                    <b style="color:#00e5ff;">CLASSES</b>&nbsp; Real &nbsp;|&nbsp; Fake
+                </div>
+                """)
+
+            # Right column — verdict + probability bars
+            with gr.Column(scale=1):
+                verdict_output = gr.Markdown(
+                    value="*Upload an image and click **Analyze** to begin.*",
+                    label="Verdict",
+                )
+                label_output = gr.Label(
+                    num_top_classes=2,
+                    label="Class Probabilities",
+                )
+
+        # ── Example images (add files to repo root to enable) ────────
+        gr.Examples(
+            examples=[],  # e.g. [["examples/real1.jpg"], ["examples/fake1.jpg"]]
+            inputs=image_input,
+            label="Example Images",
+        )
+
+        # ── Event wiring ─────────────────────────────────────────────
+        submit_btn.click(
+            fn=_predict_wrapper,
+            inputs=image_input,
+            outputs=[label_output, verdict_output],
+        )
+
+        # ── Footer ───────────────────────────────────────────────────
+        gr.HTML("""
+        <div style="text-align:center; padding:24px 0 8px;
+                    font-family:'Share Tech Mono',monospace;
+                    font-size:0.75rem; color:#2a3050;">
+            Built with ❤ &nbsp;·&nbsp; Gradio &nbsp;·&nbsp; HuggingFace Spaces &nbsp;·&nbsp; PyTorch
+        </div>
+        """)
+
+    return demo
+
+
+# ============================================================
+# MAIN
+# ============================================================
+def main() -> None:
+    """
+    Application entry point — runs the full pipeline:
+      1. Download model weights from HuggingFace Hub (if not cached).
+      2. Instantiate and load the DeepfakeModel.
+      3. Build the Gradio UI.
+      4. Launch the Space server.
+    """
+    # ── Step 1: Weights ──────────────────────────────────────────
+    download_weights()
+
+    # ── Step 2: Model ────────────────────────────────────────────
+    model = load_model()
+
+    # ── Step 3: UI ───────────────────────────────────────────────
+    demo = build_ui(model)
+
+    # ── Step 4: Launch ───────────────────────────────────────────
+    demo.launch()
+
+
+if __name__ == "__main__":
+    main()