Added application and requirements file

app.py

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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.h5")
+
+# 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()

requirements.txt

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+gradio>=3.0.0
+opencv-python
+numpy
+tensorflow
+keras