app.py

import os
import gradio as gr
import cv2
import time
import json
import random
import logging
import matplotlib.pyplot as plt
import shutil
from datetime import datetime
from collections import Counter
from typing import Any, Dict, List, Optional, Tuple
import numpy as np

os.environ["YOLO_CONFIG_DIR"] = "/tmp/Ultralytics"

try:
    from services.video_service import get_next_video_frame, reset_video_index, preload_video, release_video
    from services.metrics_service import update_metrics
    from services.salesforce_dispatcher import send_to_salesforce
    from services.shadow_detection import detect_shadow_coverage
    from services.thermal_service import process_thermal
    from services.map_service import generate_map
    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
except ImportError as e:
    print(f"Failed to import service modules: {str(e)}")
    logging.error(f"Import error: {str(e)}")
    exit(1)

logging.basicConfig(
    filename="app.log",
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s"
)

paused: bool = False
frame_rate: float = 0.3
frame_count: int = 0
log_entries: List[str] = []
detected_counts: List[int] = []
last_frame: Optional[np.ndarray] = None
last_metrics: Dict[str, Any] = {}
last_timestamp: str = ""
detected_plants: List[str] = []
gps_coordinates: List[List[float]] = []
media_loaded: bool = False
active_service: Optional[str] = None
is_video: bool = True
static_image: Optional[np.ndarray] = None
enabled_services: List[str] = []

DEFAULT_VIDEO_PATH = "sample.mp4"
TEMP_IMAGE_PATH = os.path.abspath("temp.jpg")
CAPTURED_FRAMES_DIR = os.path.abspath("captured_frames")
OUTPUT_DIR = os.path.abspath("outputs")
TEMP_MEDIA_DIR = os.path.abspath("temp_media")

for directory in [CAPTURED_FRAMES_DIR, OUTPUT_DIR, TEMP_MEDIA_DIR]:
    os.makedirs(directory, exist_ok=True)
    os.chmod(directory, 0o777)

def initialize_media(media_file: Optional[Any] = None) -> str:
    global media_loaded, is_video, static_image, log_entries, frame_count
    release_video()
    static_image = None
    frame_count = 0

    if media_file is None:
        media_path = DEFAULT_VIDEO_PATH
        log_entries.append(f"No media uploaded, attempting to load default: {media_path}")
        logging.info(f"No media uploaded, attempting to load default: {media_path}")
    else:
        if not hasattr(media_file, 'name') or not media_file.name:
            status = "Error: Invalid media file uploaded."
            log_entries.append(status)
            logging.error(status)
            media_loaded = False
            return status

        original_path = media_file.name
        file_extension = os.path.splitext(original_path)[1].lower()
        temp_media_path = os.path.join(TEMP_MEDIA_DIR, f"uploaded_media{file_extension}")
        try:
            shutil.copy(original_path, temp_media_path)
            media_path = temp_media_path
            log_entries.append(f"Copied uploaded file to: {media_path}")
            logging.info(f"Copied uploaded file to: {media_path}")
        except Exception as e:
            status = f"Error copying uploaded file: {str(e)}"
            log_entries.append(status)
            logging.error(status)
            media_loaded = False
            return status

    if not os.path.exists(media_path):
        status = f"Error: Media file '{media_path}' not found."
        log_entries.append(status)
        logging.error(status)
        media_loaded = False
        return status

    try:
        if file_extension in (".mp4", ".avi"):
            is_video = True
            preload_video(media_path)
            media_loaded = True
            status = f"Successfully loaded video: {media_path}"
        elif file_extension in (".jpg", ".jpeg", ".png"):
            is_video = False
            static_image = cv2.imread(media_path)
            if static_image is None:
                raise RuntimeError(f"Failed to load image: {media_path}")
            static_image = cv2.resize(static_image, (320, 240))
            media_loaded = True
            status = f"Successfully loaded image: {media_path}"
        else:
            media_loaded = False
            status = "Error: Unsupported file format. Use .mp4, .avi, .jpg, .jpeg, or .png."
            log_entries.append(status)
            logging.error(status)
            return status

        log_entries.append(status)
        logging.info(status)
        return status
    except Exception as e:
        media_loaded = False
        status = f"Error loading media: {str(e)}"
        log_entries.append(status)
        logging.error(status)
        return status

def set_active_service(pl_val: bool) -> Tuple[Optional[str], str]:
    global active_service, enabled_services
    enabled_services = []
    if pl_val:
        enabled_services.append("plantation")

    if not enabled_services:
        active_service = None
        log_entries.append("Plantation service disabled.")
        logging.info("Plantation service disabled.")
        return None, "No Service Enabled"

    active_service = "plantation"
    log_entries.append("Enabled service: Plantation")
    logging.info("Enabled service: Plantation")
    return active_service, "Enabled: Plantation"

def generate_line_chart() -> Optional[str]:
    if not detected_counts:
        return None
    fig, ax = plt.subplots(figsize=(4, 2))
    ax.plot(detected_counts[-50:], marker='o', color='#FF8C00')
    ax.set_title("Detections Over Time")
    ax.set_xlabel("Frame")
    ax.set_ylabel("Count")
    ax.grid(True)
    fig.tight_layout()
    chart_path = "chart_temp.png"
    try:
        fig.savefig(chart_path)
        plt.close(fig)
        return chart_path
    except Exception as e:
        log_entries.append(f"Error generating chart: {str(e)}")
        logging.error(f"Error generating chart: {str(e)}")
        return None

def monitor_feed() -> Tuple[
    Optional[np.ndarray],
    str,
    str,
    List[str],
    Optional[str],
    Optional[str]
]:
    global paused, frame_count, last_frame, last_metrics, last_timestamp
    global gps_coordinates, detected_plants, media_loaded
    global is_video, static_image, enabled_services

    if not media_loaded:
        log_entries.append("Cannot start processing: Media not loaded successfully.")
        logging.error("Media not loaded successfully.")
        return (
            None,
            json.dumps({"error": "Media not loaded. Please upload a video or image file."}, indent=2),
            "\n".join(log_entries[-10:]),
            detected_plants,
            None,
            None
        )

    if paused and last_frame is not None:
        frame = last_frame.copy()
        metrics = last_metrics.copy()
    else:
        max_retries = 3
        start_time = time.time()
        for attempt in range(max_retries):
            try:
                if is_video:
                    frame = get_next_video_frame()
                    if frame is None:
                        log_entries.append(f"Frame retrieval failed on attempt {attempt + 1}, resetting video.")
                        logging.warning(f"Frame retrieval failed on attempt {attempt + 1}, resetting video.")
                        reset_video_index()
                        continue
                    break
                else:
                    frame = static_image.copy()
                    break
            except Exception as e:
                log_entries.append(f"Frame retrieval error on attempt {attempt + 1}: {str(e)}")
                logging.error(f"Frame retrieval error on attempt {attempt + 1}: {str(e)}")
                if attempt == max_retries - 1:
                    return (
                        None,
                        json.dumps(last_metrics, indent=2),
                        "\n".join(log_entries[-10:]),
                        detected_plants,
                        None,
                        None
                    )
        else:
            log_entries.append("Failed to retrieve frame after maximum retries.")
            logging.error("Failed to retrieve frame after maximum retries.")
            return (
                None,
                json.dumps(last_metrics, indent=2),
                "\n".join(log_entries[-10:]),
                detected_plants,
                None,
                None
            )

        detection_frame = cv2.resize(frame, (512, 320))
        all_detected_items: List[Dict[str, Any]] = []
        shadow_issue = False
        thermal_flag = False

        try:
            if "plantation" in enabled_services:
                plant_dets, detection_frame = process_plants(detection_frame)
                health_dets, detection_frame = process_plant_health(detection_frame)
                missing_dets, detection_frame = process_missing_patches(detection_frame)
                all_detected_items.extend(plant_dets + health_dets + missing_dets)

            try:
                cv2.imwrite(TEMP_IMAGE_PATH, detection_frame)
                shadow_issue = detect_shadow_coverage(TEMP_IMAGE_PATH)
            except Exception as e:
                log_entries.append(f"Error saving temp image for shadow detection: {str(e)}")
                logging.error(f"Error saving temp image: {str(e)}")
                shadow_issue = False

            if len(detection_frame.shape) == 2:
                thermal_results = process_thermal(detection_frame)
                thermal_dets = thermal_results["detections"]
                detection_frame = thermal_results["frame"]
                all_detected_items.extend(thermal_dets)
                thermal_flag = bool(thermal_dets)

            orig_h, orig_w = frame.shape[:2]
            det_h, det_w = detection_frame.shape[:2]
            scale_x, scale_y = orig_w / det_w, orig_h / det_h
            for item in all_detected_items:
                if "box" in item:
                    box = item["box"]
                    item["box"] = [
                        int(box[0] * scale_x),
                        int(box[1] * scale_y),
                        int(box[2] * scale_x),
                        int(box[3] * scale_y)
                    ]

            for item in all_detected_items:
                box = item.get("box", [])
                if not box:
                    continue
                x_min, y_min, x_max, y_max = box
                label = item.get("label", "")
                dtype = item.get("type", "")
                health = item.get("health", "")

                if dtype == "plant":
                    color = (0, 255, 255)  # Cyan
                    if health == "healthy":
                        color = (255, 165, 0)  # Orange
                elif dtype == "missing_patch":
                    color = (255, 0, 255)  # Magenta
                else:
                    continue

                cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, 3)
                (text_w, text_h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)
                label_background = frame[y_min - text_h - 15:y_min - 5, x_min:x_min + text_w + 10]
                if label_background.size > 0:
                    overlay = label_background.copy()
                    cv2.rectangle(overlay, (0, 0), (text_w + 10, text_h + 10), (0, 0, 0), -1)
                    alpha = 0.5
                    cv2.addWeighted(overlay, alpha, label_background, 1 - alpha, 0, label_background)
                cv2.putText(frame, label, (x_min + 5, y_min - 10),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)

            try:
                cv2.imwrite(TEMP_IMAGE_PATH, frame, [int(cv2.IMWRITE_JPEG_QUALITY), 95])
            except Exception as e:
                log_entries.append(f"Error saving temp image: {str(e)}")
                logging.error(f"Error saving temp image: {str(e)}")

        except Exception as e:
            log_entries.append(f"Processing Error: {str(e)}")
            logging.error(f"Processing error: {str(e)}")
            all_detected_items = []

        metrics = update_metrics(all_detected_items)
        gps_coord = [17.385044 + random.uniform(-0.001, 0.001), 78.486671 + frame_count * 0.0001]
        gps_coordinates.append(gps_coord)

        for item in all_detected_items:
            item["gps"] = gps_coord

        detection_types = {item.get("type") for item in all_detected_items if "type" in item}
        if detection_types:
            try:
                captured_frame_path = os.path.join(CAPTURED_FRAMES_DIR, f"detected_{frame_count}.jpg")
                success = cv2.imwrite(captured_frame_path, frame, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
                if not success:
                    raise RuntimeError(f"Failed to save captured frame: {captured_frame_path}")
                for item in all_detected_items:
                    if item.get("type") == "plant":
                        detected_plants.append(captured_frame_path)
                        if len(detected_plants) > 100:
                            detected_plants.pop(0)
            except Exception as e:
                log_entries.append(f"Error saving captured frame: {str(e)}")
                logging.error(f"Error saving captured frame: {str(e)}")

        all_detections = {
            "detections": all_detected_items,
            "metrics": metrics,
            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "frame_count": frame_count,
            "gps_coordinates": gps_coord,
            "shadow_issue": shadow_issue,
            "thermal": thermal_flag
        }

        try:
            send_to_salesforce(all_detections)
        except Exception as e:
            log_entries.append(f"Salesforce Dispatch Error: {str(e)}")
            logging.error(f"Salesforce dispatch error: {str(e)}")

        try:
            frame_path = os.path.join(OUTPUT_DIR, f"frame_{frame_count:04d}.jpg")
            success = cv2.imwrite(frame_path, frame, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
            if not success:
                raise RuntimeError(f"Failed to save output frame: {frame_path}")
        except Exception as e:
            log_entries.append(f"Error saving output frame: {str(e)}")
            logging.error(f"Error saving output frame: {str(e)}")

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

        plant_detected = len([item for item in all_detected_items if item.get("type") == "plant"])
        missing_detected = len([item for item in all_detected_items if item.get("type") == "missing_patch"])
        detected_counts.append(plant_detected + missing_detected)

        processing_time = time.time() - start_time
        detection_summary = {
            "timestamp": last_timestamp,
            "frame": frame_count,
            "plants": plant_detected,
            "missing_patches": missing_detected,
            "gps": gps_coord,
            "processing_time_ms": processing_time * 1000
        }
        log_message = json.dumps(detection_summary, indent=2)
        log_entries.append(log_message)
        logging.info(log_message)

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

    map_items = [item for item in last_metrics.get("items", []) if item.get("type") in ["plant", "missing_patch"]]
    map_path = generate_map(gps_coordinates[-5:], map_items)

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

with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange", secondary_hue="amber")) as app:
    gr.Markdown(
        """
        # 🌳 Plantation Inspection Dashboard
        Monitor plantation conditions in real-time using drone footage or static images.
        """
    )

    with gr.Row():
        with gr.Column(scale=3):
            media_input = gr.File(label="Upload Media File (e.g., sample.mp4, image.jpg)", file_types=[".mp4", ".avi", ".jpg", ".jpeg", ".png"])
            load_button = gr.Button("Load Media", variant="primary")
        with gr.Column(scale=1):
            media_status = gr.Textbox(
                label="Media Load Status",
                value="Please upload a video/image file or ensure 'sample.mp4' exists in the root directory.",
                interactive=False
            )

    with gr.Row():
        with gr.Column():
            pl_toggle = gr.Checkbox(label="Enable Plantation Services", value=False)
            pl_status = gr.Textbox(label="Plantation Status", value="Disabled", interactive=False)

    status_text = gr.Markdown("**Status:** 🟢 Ready (Upload a media file and enable the service to start)")

    with gr.Row():
        with gr.Column(scale=3):
            media_output = gr.Image(label="Live Feed", width=640, height=480, elem_id="live-feed")
        with gr.Column(scale=1):
            metrics_output = gr.Textbox(
                label="Detection Metrics",
                lines=10,
                interactive=False,
                placeholder="Detection metrics, counts will appear here."
            )

    with gr.Row():
        with gr.Column(scale=2):
            logs_output = gr.Textbox(label="Live Logs", lines=8, interactive=False)
        with gr.Column(scale=1):
            plant_images = gr.Gallery(label="Detected Plants (Last 100+)", columns=4, rows=13, height="auto")

    with gr.Row():
        chart_output = gr.Image(label="Detection Trend")
        map_output = gr.Image(label="Issue Locations Map")

    with gr.Row():
        pause_btn = gr.Button("⏸️ Pause", variant="secondary")
        resume_btn = gr.Button("▶️ Resume", variant="primary")
        frame_slider = gr.Slider(0.05, 1.0, value=0.3, label="Frame Interval (seconds)", step=0.05)

    gr.HTML("""
    <style>
        body {
            background-color: #FFDAB9 !important;
        }
        #live-feed {
            border: 2px solid #FF8C00;
            border-radius: 10px;
        }
        .gr-button-primary {
            background-color: #FF8C00 !important;
        }
        .gr-button-secondary {
            background-color: #FF6347 !important;
        }
    </style>
    """)

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

    def toggle_resume() -> str:
        global paused
        paused = False
        return "**Status:** 🟢 Streaming"

    def set_frame_rate(val: float) -> None:
        global frame_rate
        frame_rate = val

    media_status.value = initialize_media()

    load_button.click(
        initialize_media,
        inputs=[media_input],
        outputs=[media_status]
    )

    def update_toggles(pl_val: bool) -> Tuple[str, str]:
        active, status_message = set_active_service(pl_val)
        pl_status_val = "Enabled" if pl_val else "Disabled"
        return pl_status_val, status_message

    pl_toggle.change(update_toggles, inputs=[pl_toggle], outputs=[pl_status, status_text])

    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:
            if not media_loaded:
                yield None, json.dumps({"error": "Media not loaded. Please upload a video or image file."}, indent=2), "\n".join(log_entries[-10:]), detected_plants, None, None
            else:
                frame, metrics, logs, plants, chart, map_path = monitor_feed()
                if frame is None:
                    yield None, metrics, logs, plants, chart, map_path
                else:
                    yield frame, metrics, logs, plants, chart, map_path
                if not is_video:
                    break
            time.sleep(frame_rate)

    app.load(streaming_loop, outputs=[media_output, metrics_output, logs_output, plant_images, chart_output, map_output])

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