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app.py

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+import gradio as gr
+from transformers import CLIPProcessor, CLIPModel
+from PIL import Image
+import torch
+
+print("Loading Classification Model...")
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+model.eval()
+print("Model Loaded successfully!")
+
+
+LABELS = [
+    "an official academic certificate or diploma",
+    "a government ID card, passport, or driving license",
+    "a random photograph of a person, nature, or objects",
+    "a screenshot of a website or meme"
+]
+
+def verify_document_class(image: Image.Image):
+    """
+    Returns:
+      - is_valid (bool)
+      - message (str)
+      - best_label (str)
+      - confidence (%) (float)
+      - probabilities table (list)
+    """
+    if image is None:
+        return False, "No image provided", "", 0.0, []
+
+    image = image.convert("RGB")
+
+    inputs = processor(text=LABELS, images=image, return_tensors="pt", padding=True)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = outputs.logits_per_image.softmax(dim=1)[0].cpu().numpy()
+
+    best_idx = int(probs.argmax())
+    best_label = LABELS[best_idx]
+    confidence = float(probs[best_idx] * 100.0)
+
+    # Gatekeeper rule
+    is_valid = best_idx in (0, 1)
+    message = "Valid Document Class" if is_valid else "Invalid image type. Please upload a certificate or ID."
+
+    # Build a simple table for display
+    table = [{"label": LABELS[i], "probability_%": float(probs[i] * 100.0)} for i in range(len(LABELS))]
+
+    return is_valid, message, best_label, confidence, table
+
+
+demo = gr.Interface(
+    fn=verify_document_class,
+    inputs=gr.Image(type="pil", label="Upload Image"),
+    outputs=[
+        gr.Checkbox(label="Is Valid Document?"),
+        gr.Textbox(label="Message"),
+        gr.Textbox(label="Best Match Label"),
+        gr.Number(label="Confidence (%)"),
+        gr.JSON(label="All Probabilities"),
+    ],
+    title="Document Class Verifier (CLIP Gatekeeper)",
+    description="Uploads an image and checks whether it looks like a certificate/diploma or ID vs random photo/screenshot."
+)
+
+demo.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

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+gradio
+transformers
+torch
+pillow