import gradio as gr import matplotlib.pyplot as plt from PIL import Image from ultralyticsplus import YOLO import cv2 import numpy as np import requests from io import BytesIO import os model = YOLO('Corn-Disease50epoch.pt') name = ['Leaf Blight', 'Corn Rust', 'Gray Leaf Spot', 'Healthy'] image_directory = "/home/user/app/images" def response2(image, image_size=640, conf_threshold=0.3, iou_threshold=0.6): results = model.predict(image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size) text = "" name_weap = "" solution = "" for r in results: im_array = r.plot() im = Image.fromarray(im_array[..., ::-1]) for r in results: conf = np.array(r.boxes.conf.cpu()) cls = np.array(r.boxes.cls.cpu()) cls = cls.astype(int) xywh = np.array(r.boxes.xywh.cpu()) xywh = xywh.astype(int) for con, cl, xy in zip(conf, cls, xywh): cone = con.astype(float) conef = round(cone, 3) conef = conef * 100 text += (f"Detected {name[cl]} with confidence {round(conef, 1)}% at ({xy[0]},{xy[1]})\n") if name[cl] == "Corn Rust": solution = (f"{solution} Apply fungicides with active ingredients like propiconazole or tebuconazole when symptoms appear.\n") elif name[cl] == "Gray Leaf Spot": solution = (f"{solution} Use fungicides containing strobilurins (e.g., azoxystrobin) or triazoles.\n") elif name[cl] == "Leaf Blight": solution = (f"{solution} Treat with fungicides such as mancozeb or chlorothalonil during the early stages.\n") return im, text, solution def pil_to_cv2(pil_image): open_cv_image = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR) return open_cv_image def process_video(video_path): cap = cv2.VideoCapture(video_path) while cap.isOpened(): ret, frame = cap.read() if not ret: break pil_img = Image.fromarray(frame[..., ::-1]) result = model.predict(source=pil_img) for r in result: im_array = r.plot() processed_frame = Image.fromarray(im_array[..., ::-1]) yield processed_frame cap.release() inputs = [ gr.Image(type="pil", label="Input Image"), gr.Slider(minimum=320, maximum=1280, value=640, step=32, label="Image Size"), gr.Slider(minimum=0.0, maximum=1.0, value=0.3, step=0.05, label="Confidence Threshold"), gr.Slider(minimum=0.0, maximum=1.0, value=0.6, step=0.05, label="IOU Threshold"), ] outputs = [ gr.Image(type="pil", label="Output Image"), gr.Textbox(label="Result"), gr.Textbox(label="Solution") ] examples = [ ["/home/user/app/images/jagung7.jpg", 640, 0.3, 0.6], ["/home/user/app/images/jagung4.jpeg", 640, 0.3, 0.6], ["/home/user/app/images/jagung6.jpeg", 640, 0.3, 0.6] ] title = """Corn Diseases Detection Finetuned YOLOv11

Colab """ description = 'Image Size: Defines the image size for inference.\nConfidence Treshold: Sets the minimum confidence threshold for detections.\nIOU Treshold: Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Useful for reducing duplicates.' video_iface = gr.Interface( fn=process_video, inputs=gr.Video(label="Upload Video", interactive=True), outputs=gr.Image(type="pil", label="Result"), title=title, description="Upload video for inference." ) image_iface = gr.Interface( fn=response2, inputs=inputs, outputs=outputs, examples=examples, title=title, description=description ) demo = gr.TabbedInterface([image_iface, video_iface], ["Image Inference", "Video Inference"]) if __name__ == '__main__': demo.launch()