Update app.py

app.py

@@ -2,32 +2,22 @@ import gradio as gr
 import numpy as np
 import cv2
 from keras.models import load_model
-from collections import deque
 
 # Load the trained model
 model = load_model("bullying_detection_model.keras")
 
-# Constants
+# Constants (should match your training)
 IMAGE_HEIGHT, IMAGE_WIDTH = 64, 64
 SEQUENCE_LENGTH = 16
 CLASSES_LIST = ["NonBullying", "Bullying"]
 
-frame_queue = deque(maxlen=SEQUENCE_LENGTH)
-
+# Helper to preprocess frames
 def preprocess_frames(frames):
-    return np.array([cv2.resize(f, (IMAGE_HEIGHT, IMAGE_WIDTH))/255.0 for f in frames])
-
-def predict_webcam(frame):
-    frame_queue.append(frame)
-    if len(frame_queue) < SEQUENCE_LENGTH:
-        return "Collecting frames..."
-    input_frames = preprocess_frames(list(frame_queue))
-    input_frames = np.expand_dims(input_frames, axis=0)
-    preds = model.predict(input_frames, verbose=0)[0]
-    idx = np.argmax(preds)
-    return f"Prediction: {CLASSES_LIST[idx]} (Confidence: {preds[idx]:.2f})"
+    processed = [cv2.resize(f, (IMAGE_HEIGHT, IMAGE_WIDTH))/255.0 for f in frames]
+    return np.array(processed)
 
-def predict_video(video):
+# Gradio function: receives a video, returns prediction
+def predict_bullying(video):
     cap = cv2.VideoCapture(video)
     frames = []
     while True:
@@ -38,27 +28,24 @@ def predict_video(video):
     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[i] for i in idxs]
-    input_frames = preprocess_frames(sampled)
+    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, verbose=0)[0]
-    idx = np.argmax(preds)
-    return f"Prediction: {CLASSES_LIST[idx]} (Confidence: {preds[idx]:.2f})"
-
-with gr.Blocks() as demo:
-    gr.Markdown("## Bullying Detection: Webcam & Video Upload Demo")
-    
-    with gr.Tab("Webcam"):
-        webcam = gr.Image(type="numpy", tool="editor")
-        output_webcam = gr.Label(num_top_classes=1)
-        webcam.change(predict_webcam, inputs=webcam, outputs=output_webcam, live=True)
-    
-    with gr.Tab("Video Upload"):
-        video_input = gr.Video()
-        output_video = gr.Label(num_top_classes=1)
-        btn = gr.Button("Predict")
-        btn.click(predict_video, inputs=video_input, outputs=output_video)
+    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__":
-    demo.launch()
+    iface.launch()