app.py

import gradio as gr
import cv2
import time
import os
import json
from datetime import datetime
from services.video_service import get_next_video_frame, reset_video_index, preload_video
# Under Construction services
from services.under_construction.earthwork_detection import process_earthwork
from services.under_construction.culvert_check import process_culverts
from services.under_construction.bridge_pier_check import process_bridge_piers
# Comment out other services
# from services.operations_maintenance.pothole_detection import process_potholes
# from services.operations_maintenance.crack_detection import process_cracks
# from services.operations_maintenance.signage_check import process_signages
# from services.road_safety.barrier_check import process_barriers
# from services.road_safety.lighting_check import process_lighting
# from services.road_safety.accident_spot_check import process_accident_spots
# from services.plantation.plant_count import process_plants
# from services.plantation.plant_health import process_plant_health
# from services.plantation.missing_patch_check import process_missing_patches
# Original services (not used in this mode but imported for potential future use)
from services.detection_service import process_frame as process_generic
from services.metrics_service import compute_metrics
from services.overlay_service import add_overlay
from services.salesforce_dispatcher import dispatch_to_salesforce
from services.shadow_detection import detect_shadows
from services.thermal_service import process_thermal

# Preload video
try:
    preload_video()
except Exception as e:
    print(f"Error preloading video: {str(e)}")

# Globals
paused = False
frame_rate = 0.5  # Process every 0.5 seconds for real-time feel
frame_count = 0
log_entries = []
last_frame = None
last_detections = {}
last_timestamp = ""

# Constants
TEMP_IMAGE_PATH = "temp.jpg"
OUTPUT_DIR = "outputs"
os.makedirs(OUTPUT_DIR, exist_ok=True)

def monitor_feed():
    """
    Main function to process video frames in real-time.
    """
    global paused, frame_count, last_frame, last_detections, last_timestamp

    if paused and last_frame is not None:
        frame = last_frame.copy()
        detections = last_detections.copy()
    else:
        try:
            frame = get_next_video_frame()
        except RuntimeError as e:
            log_entries.append(f"Error: {str(e)}")
            return None, json.dumps(last_detections, indent=2), "\n".join(log_entries[-10:])

        # Run Under Construction detections
        earthwork_dets, frame = process_earthwork(frame)
        culvert_dets, frame = process_culverts(frame)
        bridge_pier_dets, frame = process_bridge_piers(frame)

        # Combine detections
        all_detections = {
            "earthwork": earthwork_dets,
            "culverts": culvert_dets,
            "bridge_piers": bridge_pier_dets,
            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "frame_count": frame_count
        }

        # Compute metrics
        all_dets_list = earthwork_dets + culvert_dets + bridge_pier_dets
        metrics = compute_metrics(all_dets_list)
        all_detections["metrics"] = metrics

        # Dispatch to Salesforce
        dispatch_to_salesforce(all_detections, all_detections["timestamp"])

        # Save annotated frame
        frame_path = os.path.join(OUTPUT_DIR, f"frame_{frame_count:04d}.jpg")
        cv2.imwrite(frame_path, frame)

        frame_count += 1
        last_timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        log_entries.append(f"{last_timestamp} - Frame {frame_count} - Detections: {len(all_dets_list)} - Avg Conf: {metrics['avg_confidence']:.2f}")

        last_frame = frame.copy()
        last_detections = all_detections

        if len(log_entries) > 100:
            log_entries.pop(0)

    # Add frame count and timestamp to display
    frame = cv2.resize(last_frame, (640, 480))
    cv2.putText(frame, f"Frame: {frame_count}", (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
    cv2.putText(frame, f"{last_timestamp}", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)

    return frame[:, :, ::-1], json.dumps(last_detections, indent=2), "\n".join(log_entries[-10:])

# Gradio UI
with gr.Blocks(theme=gr.themes.Soft()) as app:
    gr.Markdown("# 🛡️ NHAI Drone Analytics Monitoring System - Under Construction")

    status_text = gr.Markdown("**Status:** 🟢 Running")

    with gr.Row():
        with gr.Column(scale=3):
            video_output = gr.Image(label="Live Drone Feed", width=640, height=480)
        with gr.Column(scale=1):
            detections_output = gr.Textbox(label="Detections", lines=10)

    with gr.Row():
        logs_output = gr.Textbox(label="Live Logs", lines=8)

    with gr.Row():
        pause_btn = gr.Button("⏸️ Pause")
        resume_btn = gr.Button("▶️ Resume")
        frame_slider = gr.Slider(0.1, 5, value=0.5, label="Frame Interval (seconds)")

    def toggle_pause():
        global paused
        paused = True
        return "**Status:** ⏸️ Paused"

    def toggle_resume():
        global paused
        paused = False
        return "**Status:** 🟢 Running"

    def set_frame_rate(val):
        global frame_rate
        frame_rate = val

    pause_btn.click(toggle_pause, outputs=status_text)
    resume_btn.click(toggle_resume, outputs=status_text)
    frame_slider.change(set_frame_rate, inputs=[frame_slider])

    def streaming_loop():
        while True:
            frame, detections, logs = monitor_feed()
            if frame is None:
                yield None, detections, logs
            else:
                yield frame, detections, logs
            time.sleep(frame_rate)

    app.load(streaming_loop, outputs=[video_output, detections_output, logs_output])

if __name__ == "__main__":
    app.launch(share=True)