Update app.py

app.py

@@ -5,7 +5,7 @@
 #os.environ["STREAMLIT_HOME"] = "./safe_streamlit_home"
 #os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"
 #os.makedirs(os.environ["STREAMLIT_HOME"], exist_ok=True)
-
+'''
 # Your other imports...
 import os
 
@@ -91,3 +91,45 @@ if uploaded_file is not None:
 
     cap.release()
 
+'''
+
+
+import os
+os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"
+os.environ["XDG_CONFIG_HOME"] = "/tmp"
+
+import streamlit as st
+import cv2
+import numpy as np
+import tempfile
+from src.preprocess import preprocess_frame
+from src.predict import run_prediction, load_trained_model
+
+st.set_page_config(layout="wide")
+st.title("🔍 Violence Detection in Video")
+st.markdown("Upload a video and let the model detect violent scenes in real-time.")
+
+uploaded_file = st.file_uploader("Upload a video", type=["mp4", "avi", "mpeg", "mov", "mpg"])
+model = load_trained_model()
+
+if uploaded_file is not None:
+    tfile = tempfile.NamedTemporaryFile(delete=False)
+    tfile.write(uploaded_file.read())
+    cap = cv2.VideoCapture(tfile.name)
+    stframe = st.empty()
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        processed = preprocess_frame(frame)
+        pred = run_prediction(model, processed)
+        label = "Violent" if pred <= 0.5 else "Non-Violent"
+        color = (0, 0, 255) if label == "Violent" else (0, 255, 0)
+
+        cv2.putText(frame, f'{label} ({pred:.2f})', (10, 30),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
+        stframe.image(frame, channels="BGR")
+
+    cap.release()