app.py

import gradio as gr
import cv2
import requests
import os
import random
from ultralytics import YOLO
import numpy as np
from collections import defaultdict

# Import the supervision library
import supervision as sv

# --- File Downloading ---
# File URLs for sample images and video
file_urls = [
    'https://huggingface.co/spaces/iamsuman/waste-detection/resolve/main/samples/mix2.jpg?download=true',
    'https://huggingface.co/spaces/iamsuman/waste-detection/resolve/main/samples/mix11.jpg?download=true',
    'https://huggingface.co/spaces/iamsuman/waste-detection/resolve/main/samples/sample_waste.mp4?download=true',
]

def download_file(url, save_name):
    """Downloads a file from a URL, overwriting if it exists."""
    print(f"Downloading from: {url}")
    try:
        response = requests.get(url, stream=True)
        response.raise_for_status()  # Check for HTTP errors
        with open(save_name, 'wb') as file:
            for chunk in response.iter_content(1024):
                file.write(chunk)
        print(f"Downloaded and overwrote: {save_name}")
    except requests.exceptions.RequestException as e:
        print(f"Error downloading {url}: {e}")

# Download sample images and video for the examples
for i, url in enumerate(file_urls):
    if 'mp4' in url:
        download_file(url, "video.mp4")
    else:
        download_file(url, f"image_{i}.jpg")

# --- Model and Class Configuration ---
# Load your custom YOLO model
# IMPORTANT: Replace 'best.pt' with the path to your model trained on the 12 classes.
model = YOLO('best.pt')

# Get class names and generate colors dynamically from the loaded model
# This is the best practice as it ensures names and colors match the model's output.
class_names = model.model.names
class_colors = {
    name: (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
    for name in class_names.values()
}

# Define paths for Gradio examples
image_example_paths = [['image_0.jpg'], ['image_1.jpg']]
video_example_path = [['video.mp4']]


# --- Image Processing Function ---
def show_preds_image(image_path):
    """Processes a single image and overlays YOLO predictions."""
    image = cv2.imread(image_path)
    outputs = model.predict(source=image_path, verbose=False)
    results = outputs[0].cpu().numpy()

    # Convert to supervision Detections object for easier handling
    detections = sv.Detections.from_ultralytics(outputs[0])

    # Annotate the image with bounding boxes and labels
    for i, (box, conf, cls) in enumerate(zip(detections.xyxy, detections.confidence, detections.class_id)):
        x1, y1, x2, y2 = map(int, box)
        class_name = class_names[cls]
        color = class_colors[class_name]
        
        # Draw bounding box
        cv2.rectangle(image, (x1, y1), (x2, y2), color=color, thickness=2, lineType=cv2.LINE_AA)
        
        # Create and display label
        label = f"{class_name}: {conf:.2f}"
        cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2, cv2.LINE_AA)

    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)


# --- Video Processing Function (with Supervision) ---
def process_video_with_two_side_bins(video_path):

    if video_path is None:
        return
    
    generator = sv.get_video_frames_generator(video_path)

    try:
        first_frame = next(generator)
    except StopIteration:
        print("No frames found in the provided video input.")
        # Option 1: Return or yield a blank frame or error image
        # For example, yield a blank black image of fixed size:
        blank_frame = np.zeros((480, 640, 3), dtype=np.uint8)
        yield cv2.cvtColor(blank_frame, cv2.COLOR_BGR2RGB)
        return
    
    first_frame = next(generator)
    frame_height, frame_width, _ = first_frame.shape

    # Define two bins: recyle and trash sides

    bins = [
        {
            "name": "Recycle Bin",
            "coords": (
                int(frame_width * 0.05),
                int(frame_height * 0.5),
                int(frame_width * 0.25),
                int(frame_height * 0.95),
            ),
            "color": (200, 16, 46),  # Blue-ish
        },
        {
            "name": "Trash Bin",
            "coords": (
                int(frame_width * 0.75),
                int(frame_height * 0.5),
                int(frame_width * 0.95),
                int(frame_height * 0.95),
            ),
            "color": (50, 50, 50),  # Red-ish
        },
    ]

    box_annotator = sv.BoxAnnotator(thickness=2)
    label_annotator = sv.LabelAnnotator(
        text_scale=1.2,  # bigger text size
        text_thickness=3,
        text_position=sv.Position.TOP_LEFT,
    )

    tracker = sv.ByteTrack()

    items_in_bins = {bin_["name"]: set() for bin_ in bins}
    class_counts_per_bin = {bin_["name"]: defaultdict(int) for bin_ in bins}

    for i, frame in enumerate(generator):
        results = model(frame, verbose=False)[0]
        detections = sv.Detections.from_ultralytics(results)
        tracked_detections = tracker.update_with_detections(detections)

        annotated_frame = frame.copy()

        # Draw bins and bigger labels
        for bin_ in bins:
            x1, y1, x2, y2 = bin_["coords"]
            color = bin_["color"]
            cv2.rectangle(annotated_frame, (x1, y1), (x2, y2), color=color, thickness=3)
            cv2.putText(
                annotated_frame,
                bin_["name"],
                (x1 + 5, y1 - 15),
                cv2.FONT_HERSHEY_SIMPLEX,
                1.5,  # bigger font
                color,
                3,
                cv2.LINE_AA,
            )

        if tracked_detections.tracker_id is None:
            yield cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
            continue

        for box, track_id, class_id in zip(
            tracked_detections.xyxy,
            tracked_detections.tracker_id,
            tracked_detections.class_id,
        ):
            x1, y1, x2, y2 = map(int, box)
            cx = (x1 + x2) // 2
            cy = (y1 + y2) // 2

            for bin_ in bins:
                bx1, by1, bx2, by2 = bin_["coords"]
                if (bx1 <= cx <= bx2) and (by1 <= cy <= by2):
                    if track_id not in items_in_bins[bin_["name"]]:
                        items_in_bins[bin_["name"]].add(track_id)
                        class_name = class_names[class_id]
                        class_counts_per_bin[bin_["name"]][class_name] += 1

        labels = [
            f"#{tid} {class_names[cid]}"
            for cid, tid in zip(tracked_detections.class_id, tracked_detections.tracker_id)
        ]

        annotated_frame = box_annotator.annotate(
            scene=annotated_frame, detections=tracked_detections
        )
        annotated_frame = label_annotator.annotate(
            scene=annotated_frame, detections=tracked_detections, labels=labels
        )

        # Show counts per bin with bigger font
        y_pos = 50
        for bin_name, class_count_dict in class_counts_per_bin.items():
            text = (
                f"{bin_name}: "
                + ", ".join(f"{cls}={count}" for cls, count in class_count_dict.items())
            )
            cv2.putText(
                annotated_frame,
                text,
                (30, y_pos),
                cv2.FONT_HERSHEY_SIMPLEX,
                1.1,  # bigger font for counts
                (255, 255, 255),
                3,
                cv2.LINE_AA,
            )
            y_pos += 40

        yield cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)



# --- Gradio Interface Setup ---
# Gradio Interface for Image Processing
interface_image = gr.Interface(
    fn=show_preds_image,
    inputs=gr.Image(type="filepath", label="Input Image"),
    outputs=gr.Image(type="numpy", label="Output Image"),
    title="Waste Detection (Image)",
    description="Upload an image to see waste detection results.",
    examples=image_example_paths,
    cache_examples=False,
)

# Gradio Interface for Video Processing
interface_video = gr.Interface(
    fn=process_video_with_two_side_bins,
    inputs=gr.Video(label="Input Video"),
    outputs=gr.Image(type="numpy", label="Output Video Stream"),
    title="Waste Tracking and Counting (Video)",
    description="Upload a video to see real-time object tracking and counting.",
    examples=video_example_path,
    cache_examples=False,
)

# Launch the Gradio App with separate tabs for each interface
gr.TabbedInterface(
    [interface_image, interface_video],
    tab_names=['Image Inference', 'Video Inference']
).queue().launch(debug=True)