Create app.py

app.py

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+import gradio as gr
+import cv2
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import torch
+
+# Load model and processor
+model_name = "haywoodsloan/ai-image-detector-deploy"
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+model.eval()
+
+def analyze_video(video):
+    cap = cv2.VideoCapture(video.name)
+    results = []
+    frame_num = 0
+    frame_interval = 10  # analyze every 10th frame
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        if frame_num % frame_interval == 0:
+            # Convert to PIL image
+            rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            pil_image = Image.fromarray(rgb)
+
+            # Run inference
+            inputs = processor(images=pil_image, return_tensors="pt")
+            with torch.no_grad():
+                outputs = model(**inputs)
+                pred = outputs.logits.argmax(-1).item()
+                label = model.config.id2label[pred]
+
+            results.append(f"Frame {frame_num}: {label}")
+
+        frame_num += 1
+
+    cap.release()
+    return "\n".join(results)
+
+# Gradio UI
+gr.Interface(
+    fn=analyze_video,
+    inputs=gr.Video(label="Upload a video"),
+    outputs=gr.Textbox(label="Detection Results"),
+    title="AI Frame Detector",
+    description="Detects whether frames in a video are AI-generated or real."
+).launch()