Create app.py

app.py

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+import cv2
+import gradio as gr
+import numpy as np
+from keras.models import load_model
+
+# Load your trained model
+model = load_model('model.h5')  # Ensure this matches the model filename in your Space
+
+def process_frame(frame):
+    # Preprocess the frame for the model
+    img = cv2.resize(frame, (64, 64))  # Adjust size based on your model input
+    img = img.astype('float32') / 255.0  # Normalize
+    img = np.expand_dims(img, axis=0)  # Add batch dimension
+
+    # Predict using the model
+    prediction = model.predict(img)
+    return prediction[0][1]  # Assuming category 1 is jumpscare
+
+def convert_video_to_dumpscare(video_path, sensitivity):
+    cap = cv2.VideoCapture(video_path)
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    
+    output_path = 'output_dumpscare.mp4'
+    out = cv2.VideoWriter(output_path, fourcc, fps, (int(cap.get(3)), int(cap.get(4))))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Check if the frame is a jumpscare
+        if process_frame(frame) > sensitivity:  # Use sensitivity slider
+            out.write(frame)  # Write frame if it's a jumpscare
+
+    cap.release()
+    out.release()
+    return output_path
+
+# Gradio interface
+def gradio_interface(video, sensitivity):
+    output_video = convert_video_to_dumpscare(video.name, sensitivity)
+    return output_video
+
+# Set up Gradio app
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Video(label="Import Video"),
+        gr.Slider(minimum=0.0, maximum=1.0, default=0.5, label="Sensitivity"),
+    ],
+    outputs=gr.Video(label="Output Dumpscare Video"),
+    title="Dumpscare Video Converter",
+    description="Upload a video, set sensitivity, and click 'Cut' to process the video.",
+    live=True
+)
+
+iface.launch()