app.py

import gradio as gr
import torch
from PIL import Image
import numpy as np

import cv2

# Download the model from Hugging Face Hub
model_path = 'local_latest.pt'

# Load the YOLOv5 model
model = torch.hub.load('ultralytics/yolov5', 'custom', path=model_path)
model.line_thickness = 3  # Set the thickness of the bounding box lines

# Print model class names
print("Model class names:", model.names)
print("Model loaded successfully.")

def detect(image):
    # Convert PIL Image to NumPy array (OpenCV uses BGR format)
    image_np = np.array(image)
    image_np = image_np[:, :, ::-1].copy()  # Convert RGB to BGR

    # Run inference
    results = model(image)

    # Get detections
    detections = results.xyxy[0]  # xyxy format for bounding boxes

    # Check if any detections were made
    if len(detections) > 0:
        print(f"Number of detections: {len(detections)}")
        for i, (*xyxy, conf, cls) in enumerate(detections):
            # Extract box coordinates and convert to integers
            xyxy = [int(coord.item()) for coord in xyxy]  # Coordinates
            conf = conf.item()  # Confidence score
            cls = int(cls.item())  # Class index
            class_name = model.names[cls]  # Class name
            print(f"Detection {i}: Class '{class_name}', Confidence {conf:.2f}, Coordinates {xyxy}")

            # Draw bounding box without label
            cv2.rectangle(
                image_np,
                (xyxy[0], xyxy[1]),
                (xyxy[2], xyxy[3]),
                color=(255, 0, 0),
                thickness=model.line_thickness,
                lineType=cv2.LINE_AA,
            )
    else:
        print("No detections were made.")

    # Convert BGR back to RGB
    image_np = image_np[:, :, ::-1]

    # Convert NumPy array back to PIL Image
    annotated_image = Image.fromarray(image_np)

    return annotated_image

# Create Gradio Interface
iface = gr.Interface(
    fn=detect,
    inputs=gr.Image(type="pil"),
    outputs=gr.Image(type="pil"),
    title="YOLOv5 Object Detection",
    description="Upload an image to detect objects using the YOLOv5 model. Labels are hidden in the output."
)

iface.launch()