app.py

import gradio as gr
import cv2
import time
import os
import random
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime
from collections import Counter
from services.video_service import get_next_video_frame, reset_video_index
from services.thermal_service import detect_thermal_anomalies
from services.overlay_service import overlay_boxes
from services.metrics_service import update_metrics

# Globals
paused = False
frame_rate = 1
frame_count = 0
log_entries = []
anomaly_counts = []
anomaly_types_all = []
last_frame = None
last_metrics = {}
last_timestamp = ""
last_detected_images = []

# Constants
TEMP_IMAGE_PATH = "temp.jpg"
CAPTURED_FRAMES_DIR = "captured_frames"
os.makedirs(CAPTURED_FRAMES_DIR, exist_ok=True)

# Core monitor function
def monitor_feed():
    global paused, frame_count, last_frame, last_metrics, last_timestamp

    if paused and last_frame is not None:
        frame = last_frame.copy()
        metrics = last_metrics.copy()
    else:
        frame = get_next_video_frame()
        detected_boxes = detect_thermal_anomalies(frame)
        frame = overlay_boxes(frame, detected_boxes)
        cv2.imwrite(TEMP_IMAGE_PATH, frame, [int(cv2.IMWRITE_JPEG_QUALITY), 95])
        metrics = update_metrics(detected_boxes)

        frame_count += 1
        last_timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

        if detected_boxes:
            captured_frame_path = os.path.join(CAPTURED_FRAMES_DIR, f"frame_{frame_count}.jpg")
            cv2.imwrite(captured_frame_path, frame)
            last_detected_images.append(captured_frame_path)
            if len(last_detected_images) > 5:
                last_detected_images.pop(0)

        last_frame = frame.copy()
        last_metrics = metrics.copy()

        # Update persistent logs and stats
        anomaly_detected = len(last_metrics.get('anomalies', []))
        anomaly_types_all.extend([
    a['label']
    for a in last_metrics.get('anomalies', [])
    if isinstance(a, dict) and 'label' in a
])

        
        log_entries.append(f"{last_timestamp} - Frame {frame_count} - Anomalies: {anomaly_detected}")
        anomaly_counts.append(anomaly_detected)

        if len(log_entries) > 100:
            log_entries.pop(0)
        if len(anomaly_counts) > 500:
            anomaly_counts.pop(0)
        if len(anomaly_types_all) > 500:
            anomaly_types_all.pop(0)

    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], last_metrics, "\n".join(log_entries[-10:]), generate_line_chart(), generate_pie_chart(), last_detected_images

# Line chart

def generate_line_chart():
    fig, ax = plt.subplots(figsize=(4, 2))
    ax.plot(anomaly_counts[-50:], marker='o')
    ax.set_title("Anomalies Over Time")
    ax.set_xlabel("Frame")
    ax.set_ylabel("Count")
    fig.tight_layout()
    chart_path = "chart_temp.png"
    fig.savefig(chart_path)
    plt.close(fig)
    return chart_path

# Pie chart for anomaly types
def generate_pie_chart():
    if not anomaly_types_all:
        return None
    fig, ax = plt.subplots(figsize=(4, 2))
    count = Counter(anomaly_types_all[-200:])
    labels, sizes = zip(*count.items())
    ax.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)
    ax.axis('equal')
    fig.tight_layout()
    pie_path = "pie_temp.png"
    fig.savefig(pie_path)
    plt.close(fig)
    return pie_path

# Gradio UI
with gr.Blocks(theme=gr.themes.Soft()) as app:
    gr.Markdown("# 🛡️ Command Room Dashboard: Thermal Anomaly Monitoring")

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

    with gr.Row():
        with gr.Column(scale=3):
            video_output = gr.Image(label="Live Video Feed", width=640, height=480)
        with gr.Column(scale=1):
            metrics_output = gr.Textbox(label="Live Metrics", lines=4)

    with gr.Row():
        logs_output = gr.Textbox(label="Live Logs", lines=8)
        chart_output = gr.Image(label="Detection Trend")
        pie_output = gr.Image(label="Anomaly Types")

    with gr.Row():
        captured_images = gr.Gallery(label="Captured Events (Last 5)")

    with gr.Row():
        pause_btn = gr.Button("⏸️ Pause")
        resume_btn = gr.Button("▶️ Resume")
        frame_slider = gr.Slider(0.0005, 5, value=1, 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, metrics, logs, chart, pie, captured = monitor_feed()
            # Format metrics display
        yield frame, str(metrics), logs, chart, pie, captured
            time.sleep(frame_rate)

    app.load(streaming_loop, outputs=[video_output, metrics_output, logs_output, chart_output, pie_output, captured_images])

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