app.py

import gradio as gr
import cv2
import requests
import os
import random
from ultralytics import YOLO

# Define class names based on YOLO labels
class_names = {0: 'AluCan', 1: 'Glass', 2: 'PET', 3: 'HDPEM'}

# Generate random colors for each class
class_colors = {cls: (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) for cls in class_names}

# File URLs for sample images and video
file_urls = [
    'https://huggingface.co/spaces/iamsuman/ripe-and-unripe-tomatoes-detection/resolve/main/samples/AluCan1,000.jpg?download=true',
    'https://huggingface.co/spaces/iamsuman/ripe-and-unripe-tomatoes-detection/resolve/main/samples/Glass847.jpg?download=true',
    'https://huggingface.co/spaces/iamsuman/ripe-and-unripe-tomatoes-detection/resolve/main/samples/sample_waste.mp4?download=true',
]

# Function to download files
def download_file(url, save_name):
    if not os.path.exists(save_name):
        file = requests.get(url)
        open(save_name, 'wb').write(file.content)

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

# Load YOLO model
model = YOLO('best.pt')

# Sample paths
path = [['image_0.jpg'], ['image_1.jpg']]
video_path = [['video.mp4']]

# Function to process and display predictions on images
def show_preds_image(image_path):
    image = cv2.imread(image_path)
    outputs = model.predict(source=image_path)
    results = outputs[0].cpu().numpy()

    boxes = results.boxes
    names = model.model.names

    for box, conf, cls in zip(boxes.xyxy, boxes.conf, boxes.cls):
        x1, y1, x2, y2 = map(int, box)

        class_name = names[int(cls)]
        color = class_colors.get(int(cls), (255, 255, 255))  # Default to white if class is unknown

        # Draw bounding box
        cv2.rectangle(image, (x1, y1), (x2, y2), color=color, thickness=2, lineType=cv2.LINE_AA)

        # Display class label
        label = f"{class_name.capitalize()}: {conf:.2f}"
        cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color, 2, cv2.LINE_AA)

    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Function to process and display predictions on video
def show_preds_video(video_path):
    cap = cv2.VideoCapture(video_path)
    
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        
        frame_copy = frame.copy()
        outputs = model.predict(source=frame)
        results = outputs[0].cpu().numpy()
        
        boxes = results.boxes
        confidences = boxes.conf
        classes = boxes.cls
        names = model.model.names

        for box, conf, cls in zip(boxes.xyxy, confidences, classes):
            x1, y1, x2, y2 = map(int, box)

            class_name = names[int(cls)]
            color = class_colors.get(int(cls), (255, 255, 255))  # Default to white if class is unknown

            # Draw bounding box
            cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color=color, thickness=2, lineType=cv2.LINE_AA)

            # Display class label
            label = f"{class_name.capitalize()}: {conf:.2f}"
            cv2.putText(frame_copy, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1, cv2.LINE_AA)

        yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)

    cap.release()

# Gradio Image Interface
inputs_image = [gr.Image(type="filepath", label="Input Image")]
outputs_image = [gr.Image(type="numpy", label="Output Image")]
interface_image = gr.Interface(
    fn=show_preds_image,
    inputs=inputs_image,
    outputs=outputs_image,
    title="Waste Detection",
    examples=path,
    cache_examples=False,
)

# Gradio Video Interface
inputs_video = [gr.Video(label="Input Video")]
outputs_video = [gr.Image(type="numpy", label="Output Image")]
interface_video = gr.Interface(
    fn=show_preds_video,
    inputs=inputs_video,
    outputs=outputs_video,
    title="Waste Detection",
    examples=video_path,
    cache_examples=False,
)

# Launch Gradio App
gr.TabbedInterface(
    [interface_image, interface_video],
    tab_names=['Image Inference', 'Video Inference']
).queue().launch()