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 services.video_service import get_next_video_frame
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 = []

# Constants
TEMP_IMAGE_PATH = "temp.jpg"

# Core monitor function
def monitor_feed():
    global paused
    global frame_count

    frame = None

    if paused:
        if os.path.exists(TEMP_IMAGE_PATH):
            frame = cv2.imread(TEMP_IMAGE_PATH)

    if frame is None:
        frame = get_next_video_frame()

    if not paused:
        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)
    else:
        metrics = update_metrics([])

    frame = cv2.resize(frame, (640, 480))  # Fixed window size

    # Add frame count and timestamp
    frame_count += 1
    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    cv2.putText(frame, f"Frame: {frame_count}", (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
    cv2.putText(frame, f"{timestamp}", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)

    # Update logs and anomaly counts
    anomaly_detected = metrics["anomalies_detected"]
    log_entries.append(f"{timestamp} - Frame {frame_count} - Anomalies Detected: {anomaly_detected}")
    anomaly_counts.append(anomaly_detected)

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

    # THIS IS IMPORTANT FIX 👇
    label_output = {"Anomalies": anomaly_detected}

    return frame[:, :, ::-1], label_output, "\n".join(log_entries[-10:]), generate_chart()


# Chart generator
def generate_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

# Gradio UI
with gr.Blocks(theme=gr.themes.Soft()) as app:
    gr.Markdown("# 🌐 Thermal Anomaly Monitoring Dashboard", elem_id="main-title")

    status_text = gr.Markdown("**Status:** 🟢 Running", elem_id="status-banner")

    with gr.Row():
        with gr.Column(scale=3):
            video_output = gr.Image(label="Live Video Feed", elem_id="video-feed", width=640, height=480)
        with gr.Column(scale=1):
            metrics_output = gr.Label(label="Live Metrics", elem_id="metrics")

    with gr.Row():
        with gr.Column():
            logs_output = gr.Textbox(label="Live Logs", lines=10)
        with gr.Column():
            chart_output = gr.Image(label="Detection Trends")

    with gr.Row():
        pause_btn = gr.Button("⏸️ Pause")
        resume_btn = gr.Button("▶️ Resume")
        frame_slider = gr.Slider(0.2, 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 = monitor_feed()
            yield frame, metrics, logs, chart
            time.sleep(frame_rate)

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

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