Update app.py

app.py

@@ -1,58 +1,52 @@
 import gradio as gr
 import cv2
 import numpy as np
-import tensorflow as tf
+from keras.models import load_model
 
-# Load your model
-model = tf.keras.models.load_model('model.h5')  # No path needed if it's in the same directory
+# Load the pre-trained model
+model = load_model('model.h5')
 
-# Function to resize frames
-def resize_frame(frame, size=(64, 64)):
-    return cv2.resize(frame, size)
-
-# Function to process each frame
 def process_frame(frame):
-    # Resize the frame
-    resized_frame = resize_frame(frame)
-
-    # Normalize and prepare the frame for the model
-    img = resized_frame.astype('float32') / 255.0
-    img = np.expand_dims(img, axis=0)
-    img = img.reshape(1, -1)  # Flatten to match the input shape
-
-    prediction = model.predict(img)
-    return prediction[0][1]  # Assuming category 1 is jumpscare
-
-# Function to convert video to dumpscare
-def convert_video_to_dumpscare(video_path, sensitivity):
-    cap = cv2.VideoCapture(video_path)
-    jumpscare_frames = []
-
+    # Resize the image to (128, 128)
+    resized_frame = cv2.resize(frame, (128, 128))  # Resize to 128x128
+    resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2RGB)  # Convert to RGB
+    img_array = np.array(resized_frame) / 255.0  # Normalize pixel values
+    img_array = img_array.reshape(1, 128, 128, 3)  # Reshape for model input
+    
+    prediction = model.predict(img_array)  # Get prediction
+    return prediction
+
+def convert_video_to_dumpscare(video, sensitivity):
+    # Initialize the output
+    output_frames = []
+
+    # Read the video
+    cap = cv2.VideoCapture(video)
     while cap.isOpened():
         ret, frame = cap.read()
         if not ret:
             break
+        
+        # Process each frame
         prediction = process_frame(frame)
-        if prediction > sensitivity:  # Adjust this threshold as needed
-            jumpscare_frames.append(frame)
+        output_frames.append(prediction)
 
     cap.release()
+    return output_frames
 
-    # Here you can save jumpscare frames or create a new video
-    return "Dumpscare video created successfully!"  # Change this as needed
-
-# Gradio interface
 def gradio_interface(video, sensitivity):
     result = convert_video_to_dumpscare(video, sensitivity)
     return result
 
-with gr.Blocks() as demo:
-    gr.Markdown("## Video Dumpscare Generator")
-    video_input = gr.Video(label="Upload Video")
-    sensitivity_input = gr.Slider(minimum=0, maximum=1, label="Sensitivity", value=0.5)
-    submit_btn = gr.Button("Cut Video")
-    output_text = gr.Textbox(label="Output")
-
-    submit_btn.click(gradio_interface, inputs=[video_input, sensitivity_input], outputs=output_text)
-
-demo.launch()
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Video(label="Import Video"),  # Ensure video input
+        gr.Slider(label="Sensitivity", minimum=0, maximum=100, step=1, default=50)
+    ],
+    outputs="text"  # Change this according to your desired output format
+)
+
+# Launch the app
+iface.launch()