Create app.py

app.py

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+# First, make sure you've installed required packages
+# !pip install -U gradio transformers torch torchvision
+
+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+import requests
+import torch
+
+# Load the pipeline (auto-detects CUDA if available)
+device = 0 if torch.cuda.is_available() else -1
+pipe = pipeline("image-classification", model="prithivMLmods/Deep-Fake-Detector-v2-Model", device=device)
+
+def classify_image(image=None, url=None):
+    if image is None and not url:
+        return "Skill issue: You gave me nothing to work with."
+
+    try:
+        if url:
+            image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+        elif image:
+            image = Image.fromarray(image).convert("RGB")
+    except Exception as e:
+        return f"Bro... that ain't an image: {str(e)}"
+
+    result = pipe(image)
+    return {entry["label"]: round(entry["score"], 3) for entry in result}
+
+# Set up the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🔍 DeepFake Detector\nUpload an image or paste a URL. Let's see if you're being catfished.")
+
+    with gr.Row():
+        image_input = gr.Image(type="numpy", label="Upload Image")
+        url_input = gr.Textbox(label="Or Enter Image URL")
+
+    submit_btn = gr.Button("🚨 Detect")
+
+    output = gr.Label(num_top_classes=2)
+
+    submit_btn.click(fn=classify_image, inputs=[image_input, url_input], outputs=output)
+
+# Launch the app
+demo.launch()