Create app.py

app.py

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+import os
+import urllib.request
+import numpy as np
+import cv2
+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")
+
+# =====================
+# MODEL DEFINITION
+# =====================
+class DeepfakeModel(nn.Module):
+    def __init__(self):
+        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):
+        f = self.backbone.forward_features(x)
+        if len(f.shape) == 4:
+            f = f.flatten(2).mean(-1)
+        return self.classifier(f)
+
+
+# =====================
+# DOWNLOAD + LOAD
+# =====================
+def download_model():
+    if not os.path.exists(MODEL_PATH):
+        print("Downloading model weights…")
+        urllib.request.urlretrieve(MODEL_URL, MODEL_PATH)
+        print("Download complete.")
+
+download_model()
+
+model = DeepfakeModel().to(DEVICE)
+model.load_state_dict(torch.load(MODEL_PATH, map_location=DEVICE))
+model.eval()
+print("Model loaded successfully.")
+
+# =====================
+# TRANSFORM
+# =====================
+transform = transforms.Compose([
+    transforms.Resize((IMG_SIZE, IMG_SIZE)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.5] * 3, [0.5] * 3),
+])
+
+# =====================
+# INFERENCE
+# =====================
+def predict(image: Image.Image):
+    if image is None:
+        return {"Error": 1.0}, "⚠️ Please upload an image."
+
+    img_tensor = transform(image.convert("RGB")).unsqueeze(0).to(DEVICE)
+
+    with torch.no_grad():
+        logits = model(img_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    = "🔴 DEEPFAKE DETECTED"
+        verdict_md = f"## {verdict}\n**Confidence:** {confidence:.1f}%"
+    else:
+        verdict    = "🟢 LIKELY REAL"
+        verdict_md = f"## {verdict}\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;
+}
+
+/* ---- header ---- */
+.gr-header {
+    background: transparent !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;
+}
+
+/* ---- panels ---- */
+.gr-box, .gr-panel, .gr-form {
+    background: var(--surface) !important;
+    border: 1px solid var(--border) !important;
+    border-radius: var(--radius) !important;
+}
+
+/* ---- upload zone ---- */
+.gr-image, .svelte-1n8nu59 {
+    border: 2px dashed var(--border) !important;
+    border-radius: var(--radius) !important;
+    background: #0d0f14 !important;
+}
+
+/* ---- buttons ---- */
+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;
+}
+
+/* ---- labels / markdown output ---- */
+.gr-markdown h2 {
+    font-family: 'Syne', sans-serif;
+    font-size: 1.4rem;
+    font-weight: 700;
+    margin: 0 0 4px;
+}
+
+/* ---- confidence bars ---- */
+.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;
+}
+
+/* confidence fill colors */
+.gr-label .bar {
+    background: linear-gradient(90deg, var(--accent), #7b61ff) !important;
+}
+
+/* ---- footer ---- */
+footer { display: none !important; }
+"""
+
+# =====================
+# GRADIO UI
+# =====================
+with gr.Blocks(css=CSS, title="DeepFake Detector") as demo:
+
+    gr.HTML("""
+    <div style="text-align:center; padding: 32px 0 16px;">
+        <h1 class='title-heading'>DEEPFAKE DETECTOR</h1>
+        <p class='subtitle'>ConvNeXt-Base · Trained on RVF Faces · Hackathon Edition</p>
+    </div>
+    """)
+
+    with gr.Row():
+        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([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:1.7;">
+                <b style="color:#00e5ff;">MODEL</b>  ConvNeXt-Base + custom head<br>
+                <b style="color:#00e5ff;">TRAINED</b>  Real vs Fake Faces (80/20 split)<br>
+                <b style="color:#00e5ff;">INPUT</b>   224 × 224 · RGB · normalized<br>
+                <b style="color:#00e5ff;">CLASSES</b> Real · Fake
+            </div>
+            """)
+
+        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 (optional — works if you add them to the Space repo)
+    gr.Examples(
+        examples=[],          # add paths like [["examples/real1.jpg"], ["examples/fake1.jpg"]]
+        inputs=image_input,
+        label="Example Images",
+    )
+
+    submit_btn.click(
+        fn=predict,
+        inputs=image_input,
+        outputs=[label_output, verdict_output],
+    )
+
+    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 ❤ · Gradio · HuggingFace Spaces · PyTorch
+    </div>
+    """)
+
+demo.launch()