# app.py

import gradio as gr
import cv2
import os
from services.video_service import get_video_frame
from services.detection_service import detect_objects
from services.thermal_service import detect_thermal_anomalies
from services.shadow_detection import detect_shadow_coverage
from services.salesforce_dispatcher import send_to_salesforce

# Load video generator
frame_gen = get_video_frame("data/drone_day.mp4")

# Ensure temp directory exists
os.makedirs("temp", exist_ok=True)

def monitor_feed():
    try:
        frame = next(frame_gen)

        # Save frame as temporary image
        temp_path = "temp/temp_frame.jpg"
        cv2.imwrite(temp_path, frame)

        # Object Detection
        detections = detect_objects(temp_path)

        # Thermal Detection
        thermal_boxes = detect_thermal_anomalies(temp_path)

        # Shadow Detection
        shadow_flag = detect_shadow_coverage(temp_path)

        # Draw bounding boxes for visualization
        for d in detections:
            box = d['box']
            label = d['label']
            score = d['score']
            x0, y0, x1, y1 = int(box['xmin']), int(box['ymin']), int(box['xmax']), int(box['ymax'])
            cv2.rectangle(frame, (x0, y0), (x1, y1), (0, 255, 0), 2)
            cv2.putText(frame, f"{label} {score:.2f}", (x0, y0-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

        for box in thermal_boxes:
            x0, y0, x1, y1 = int(box['xmin']), int(box['ymin']), int(box['xmax']), int(box['ymax'])
            cv2.rectangle(frame, (x0, y0), (x1, y1), (0, 0, 255), 2)
            cv2.putText(frame, "Thermal", (x0, y1+20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

        # Prepare payload to send
        alert_payload = {
            "detections": detections,
            "thermal": bool(thermal_boxes),
            "shadow_issue": shadow_flag,
        }

        # 🔥 IMPORTANT: SALESFORCE API IS DISABLED FOR NOW
        # send_to_salesforce(alert_payload)
        # ⚠️ TODO: Update SALESFORCE_WEBHOOK_URL in salesforce_dispatcher.py

        return frame

    except StopIteration:
        return None

# Build Gradio UI
iface = gr.Interface(
    fn=monitor_feed,
    inputs=[],
    outputs="image",
    live=True,
    title="Solar Surveillance Feed Simulation"
)

iface.launch()