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import gradio as gr
import numpy as np
import cv2
from keras.models import load_model
# Load the trained model
model = load_model("bullying_detection_model.keras")
# Constants (should match your training)
IMAGE_HEIGHT, IMAGE_WIDTH = 64, 64
SEQUENCE_LENGTH = 16
CLASSES_LIST = ["NonBullying", "Bullying"]
# Helper to preprocess frames
def preprocess_frames(frames):
processed = [cv2.resize(f, (IMAGE_HEIGHT, IMAGE_WIDTH))/255.0 for f in frames]
return np.array(processed)
# Gradio function: receives a video, returns prediction
def predict_bullying(video):
cap = cv2.VideoCapture(video)
frames = []
while True:
ret, frame = cap.read()
if not ret:
break
frames.append(frame)
cap.release()
if len(frames) < SEQUENCE_LENGTH:
return "Video too short"
# Sample SEQUENCE_LENGTH evenly spaced frames
idxs = np.linspace(0, len(frames)-1, SEQUENCE_LENGTH).astype(int)
sampled_frames = [frames[i] for i in idxs]
input_frames = preprocess_frames(sampled_frames)
input_frames = np.expand_dims(input_frames, axis=0)
preds = model.predict(input_frames)[0]
pred_idx = np.argmax(preds)
pred_class = CLASSES_LIST[pred_idx]
confidence = float(preds[pred_idx])
return f"Prediction: {pred_class} (Confidence: {confidence:.2f})"
iface = gr.Interface(
fn=predict_bullying,
inputs=gr.Video(sources=["webcam", "upload"], label="Input Video"),
outputs="text",
title="Bullying Detection in Video",
description="Upload a video or use your webcam. The model will predict if bullying is present."
)
if __name__ == "__main__":
iface.launch() |