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eb3afa1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | import os
import sys
import time
import tempfile
import uuid
import torch
import numpy as np
from flask import Blueprint, request, jsonify, current_app
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from config import config
from utils.video_utils import load_video_frames, normalize_frames, validate_video
predict_bp = Blueprint("predict", __name__)
ALLOWED_EXTENSIONS = {".mp4", ".avi", ".mkv", ".mov", ".webm"}
def _allowed_file(filename: str) -> bool:
return os.path.splitext(filename.lower())[1] in ALLOWED_EXTENSIONS
@predict_bp.route("/predict", methods=["POST"])
def predict():
# ── Validate upload ───────────────────────────────────────────────────────
if "video" not in request.files:
return jsonify({"error": "No video file provided"}), 400
file = request.files["video"]
if file.filename == "":
return jsonify({"error": "Empty filename"}), 400
if not _allowed_file(file.filename):
exts = ", ".join(ALLOWED_EXTENSIONS)
return jsonify({"error": f"Unsupported format. Allowed: {exts}"}), 415
# ── Save to temp path ─────────────────────────────────────────────────────
suffix = os.path.splitext(file.filename)[1]
tmp_path = os.path.join(tempfile.gettempdir(), f"aivd_{uuid.uuid4().hex}{suffix}")
try:
file.save(tmp_path)
except Exception as e:
return jsonify({"error": f"Could not save file: {e}"}), 500
# ── Inference ─────────────────────────────────────────────────────────────
t_start = time.time()
try:
model = current_app.model
device = current_app.device
if not validate_video(tmp_path):
return jsonify({"error": "Invalid or corrupted video file"}), 422
frames = load_video_frames(
tmp_path,
num_frames=config.FRAMES_PER_VIDEO,
frame_size=(config.FRAME_HEIGHT, config.FRAME_WIDTH),
frame_skip=config.FRAME_SKIP,
)
if frames is None:
return jsonify({"error": "Could not extract frames from video"}), 422
frames = normalize_frames(frames)
frames_tensor = torch.from_numpy(frames).float()
frames_tensor = frames_tensor.permute(0, 3, 1, 2) # (T, 3, H, W)
frames_tensor = frames_tensor.unsqueeze(0) # (1, T, 3, H, W)
frames_tensor = frames_tensor.to(device)
with torch.no_grad():
output = model(frames_tensor)
prob = torch.sigmoid(output).item()
threshold = config.PREDICTION_THRESHOLD
is_ai = prob > threshold
verdict = "AI GENERATED" if is_ai else "REAL VIDEO"
confidence = prob if is_ai else (1.0 - prob)
elapsed = round(time.time() - t_start, 2)
return jsonify({
"verdict": verdict,
"is_ai": is_ai,
"probability": round(prob, 6),
"confidence": round(confidence * 100, 2),
"processing_time": elapsed,
"threshold": threshold,
})
except Exception as e:
return jsonify({"error": str(e)}), 500
finally:
try:
os.remove(tmp_path)
except OSError:
pass
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