app.py

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()