Delete src/app.py

src/app.py

@@ -1,65 +0,0 @@
-
-# Streamlit app will be generated here
-#import os
-
-# ✅ Set config to avoid writing to root dir
-#os.environ["STREAMLIT_HOME"] = os.path.join(os.getcwd(), ".streamlit_safe")
-#os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"
-#os.makedirs(os.environ["STREAMLIT_HOME"], exist_ok=True)
-
-#import os
-#import streamlit as st
-
-# Fix permission error for Hugging Face Spaces
-#os.environ["STREAMLIT_HOME"] = "./safe_streamlit_home"
-#os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"
-#os.makedirs(os.environ["STREAMLIT_HOME"], exist_ok=True)
-#import streamlit as st
-# Your other imports...
-import os
-
-# ✅ Tell Streamlit NOT to collect usage data or write config files
-os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"
-os.environ["XDG_CONFIG_HOME"] = "/tmp"
-
-import streamlit as st
-
-from src.process import preprocess_frame
-from src.predict import load_trained_model, predict_violence
-
-import streamlit as st
-import cv2
-import numpy as np
-from src.preprocess import preprocess_frame
-from src.predict import load_trained_model, predict_violence
-import tempfile
-
-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"])
-model = load_trained_model("models/violence_model.h5")
-
-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 = predict_violence(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()
-