import os
import sys
import tempfile
import cv2
import numpy as np
import streamlit as st

# Append src directory to import custom modules
sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))

# Environment fixes for Hugging Face Spaces
os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"
os.environ["XDG_CONFIG_HOME"] = "/tmp"
os.environ["HF_HUB_CACHE"] = "/tmp/huggingface"

# Local utility imports
from preprocess import preprocess_frame
from predict import run_prediction, load_trained_model

# Streamlit setup
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.")

# Load model with proper feedback
st.info("🔁 Loading model...")
try:
    model = load_trained_model()
    st.success("✅ Model loaded successfully.")
except Exception as e:
    st.error(f"❌ Model loading failed: {e}")
    st.stop()

# Upload video section
uploaded_file = st.file_uploader("📤 Upload a video", type=["mp4", "avi", "mpeg", "mov", "mpg"])

if uploaded_file is not None:
    try:
        st.info("📥 Reading and saving uploaded file...")

        # Read file into memory first to avoid Hugging Face frontend 403 issues
        file_bytes = uploaded_file.read()
        if not file_bytes:
            st.error("❌ Uploaded file could not be read.")
            st.stop()

        with tempfile.NamedTemporaryFile(delete=False, dir='/tmp', suffix='.mp4') as tfile:
            tfile.write(file_bytes)
            video_path = tfile.name

        st.success("✅ Video saved. Loading...")

        cap = cv2.VideoCapture(video_path)

        if not cap.isOpened():
            st.error("❌ Could not open video. Please try another format.")
            st.stop()

        stframe = st.empty()
        frame_count = 0

        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break

            frame_count += 1
            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()
        st.success(f"✅ Done! {frame_count} frames processed.")

    except Exception as e:
        st.error(f"❌ An unexpected error occurred:\n\n{e}")