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from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
import cv2
import os
import gradio as gr
# Load model and processor
model_name = "prithivMLmods/deepfake-detector-model-v1"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated label mapping
id2label = {
"0": "fake",
"1": "real"
}
def classify_image(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
def sliceFrames(cap):
frame_count = 0
frames = []
while True:
ret, frame = cap.read()
if not ret:
break
# Save every 10th frame
if frame_count % 10 == 0:
frames.append(frame)
frame_count += 1
cap.release()
return frames
def classify_video(video_path):
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
return {"error": "Could not open video."}
frames = sliceFrames(cap)
totalfake = 0
totalreal = 0
for frame in frames:
prediction = classify_image(frame)
totalfake += prediction["fake"]
totalreal += prediction["real"]
avg_fake = totalfake / len(frames) if frames else 0
avg_real = totalreal / len(frames) if frames else 0
return {
"average_fake": round(avg_fake, 3),
"average_real": round(avg_real, 3),
}
# Gradio Interface
def gradio_interface(video_file):
return classify_video(video_file)
iface = gr.Interface(
fn=gradio_interface,
inputs=gr.Video(label="Upload a video"),
outputs=gr.JSON(label="Prediction"),
title="Deepfake Detector",
description="Upload a video to check if it's real or fake."
)
if __name__ == "__main__":
iface.launch()
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