app.py

#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)

# 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


#hf_token = os.getenv("HF_token")  # Match the exact name you set in the Secrets tab

# Example usage with requests
#headers = {
   # "Authorization": f"Bearer {hf_token}"
#}

# Example: Access a private file or API on Hugging Face
#import requests
#response = requests.get("https://huggingface.co/api/whoami-v2", headers=headers)

#print(response.json())

import os
token = os.getenv("app")  # secure
from huggingface_hub import hf_hub_download

#hf_hub_download(
    #repo_id="muskan19/Violence_Detector",
    #filename="violence_model.h5",
    #use_auth_token=token
#)






import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))


# Streamlit app will be generated here
import streamlit as st
import cv2
import numpy as np
from preprocess import preprocess_frame
#from predict import run_prediction
from predict import run_prediction, load_trained_model


#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","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()