Upload 6 files

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+NV_1.mp4 filter=lfs diff=lfs merge=lfs -text
+NV_11.mp4 filter=lfs diff=lfs merge=lfs -text
+V_1000.mp4 filter=lfs diff=lfs merge=lfs -text
+V_102.mp4 filter=lfs diff=lfs merge=lfs -text

NV_1.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dc12a32b655938561b38f0bae13bc45921322d6a03d8bd57764c085814e037b7
+size 7982710

NV_11.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:18b26dffa924186e0786009a14f792aee7747de9bf6ce96b0643e08415323c8b
+size 1660244

V_1000.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:41142ab86a7e0d07be1af1a9d359430d1eb4f4cd7e968811ea02f2d4ffd78526
+size 263006

V_102.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6f661a0b0b2de8aebb060f487b7b2f00fe415e02086cff6e1fe85713cb7de40b
+size 505510

app.py

@@ -0,0 +1,80 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import cv2
+import os
+
+# Load model
+model = tf.keras.models.load_model('violence-keras-default-v1/best_model.keras')
+
+# Video processor class
+class SingleVideoProcessor:
+    def __init__(self, video_path, max_frames=50, frame_size=(128, 128), frame_interval=3):
+        self.video_path = video_path
+        self.max_frames = max_frames
+        self.frame_size = frame_size
+        self.frame_interval = frame_interval
+
+    def process(self):
+        frames = self.load_video_frames()
+        frames = self.resize_frames(frames)
+        frames = self.pad_video_frames(frames)
+        return np.array(frames, dtype=np.float32)
+
+    def load_video_frames(self):
+        frames = []
+        cap = cv2.VideoCapture(self.video_path)
+        frame_count = 0
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+            frame_count += 1
+            if frame_count % self.frame_interval == 0:
+                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                frames.append(frame)
+        cap.release()
+        return frames
+
+    def resize_frames(self, frames):
+        return [cv2.resize(frame, self.frame_size) for frame in frames]
+
+    def pad_video_frames(self, frames):
+        if len(frames) < self.max_frames:
+            padding = self.max_frames - len(frames)
+            frames.extend([np.zeros_like(frames[0])] * padding)
+        else:
+            frames = frames[:self.max_frames]
+        return frames
+
+# Prediction function
+def predict_violence(video_path):
+    processor = SingleVideoProcessor(video_path)
+    processed_video = processor.process()
+    processed_video = np.expand_dims(processed_video, axis=0)
+    prediction = model.predict(processed_video)[0][0]
+
+    label = "Violence" if prediction >= 0.5 else "Non-Violence"
+    confidence = f"{prediction:.4f}"
+    print(prediction)
+    return f"{label} (Confidence: {confidence})"
+
+# Example videos (ensure these exist in your working directory)
+examples = [
+    ["NV_1.mp4"],
+    ["V_1000.mp4"],
+    ["V_102.mp4"],
+    ["NV_11.mp4"]
+]
+
+# Launch Gradio Interface
+demo = gr.Interface(
+    fn=predict_violence,
+    inputs=gr.Video(label="Upload or Select a Video"),
+    outputs=gr.Text(label="Prediction"),
+    examples=examples,
+    title="Violence Detection in Video",
+    description="This model classifies videos as Violence or Non-Violence. Upload a short clip or select from examples."
+)
+
+demo.launch()

violence-keras-default-v1.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d2ade3703ceeeefc767446b08c80a6471627ac82e0a887f8d5f61e8e84ba69ba
+size 4509603