import streamlit as st
import cv2
from services.video_service import process_video
from services.detection_service import detect_faults_solar, detect_faults_windmill
from services.thermal_service import detect_hotspots
from services.shadow_detection import detect_shadows
from PIL import Image
import numpy as np

# Load models for solar and windmill detection
from models.solar_model import load_solar_model
from models.windmill_model import load_windmill_model

def main():
    st.title("Solar Panel and Windmill Fault Detection")
    
    # Upload video file
    uploaded_file = st.file_uploader("Upload a Video (MP4 or AVI)", type=["mp4", "avi"])

    if uploaded_file is not None:
        video_path = "/tmp/" + uploaded_file.name
        with open(video_path, "wb") as f:
            f.write(uploaded_file.read())

        # Open the video using OpenCV
        cap = cv2.VideoCapture(video_path)
        
        # Check if video was opened successfully
        if not cap.isOpened():
            st.error("Error opening video stream or file")
            return
        
        # Process frames in real-time
        stframe = st.empty()
        
        # Choose fault detection type
        choice = st.selectbox("Choose Fault Detection", ["Solar Panel", "Windmill"])
        model = None
        
        if choice == "Solar Panel":
            model = load_solar_model()
        else:
            model = load_windmill_model()

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

            # Convert the frame to RGB (Streamlit uses RGB)
            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

            # Preprocess the frame and detect faults
            faults = detect_faults_solar(model, frame_rgb) if choice == "Solar Panel" else detect_faults_windmill(model, frame_rgb)

            # Show faults on the frame
            for fault in faults:
                # For example, draw bounding boxes for faults on the frame
                x, y = int(fault['location'][0]), int(fault['location'][1])
                cv2.putText(frame, f"Fault: {fault['type']}", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
                cv2.rectangle(frame, (x-20, y-20), (x+80, y+40), (0, 0, 255), 2)

            # Update the live feed with the processed frame
            stframe.image(frame_rgb, channels="RGB", use_column_width=True)

        cap.release()

if __name__ == "__main__":
    main()