Delete app.py
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app.py
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#app7.py
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import gradio as gr
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import torch
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from PIL import Image
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from torchvision import transforms
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from ultralyticsplus import YOLO, render_result
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# import matplotlib.pyplot as plt
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import numpy as np
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torch.hub.download_url_to_file(
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'https://i.postimg.cc/g2xGJ4Qs/NSTA-Test-IMG-3276.jpg', 'NSTA.jpg')
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torch.hub.download_url_to_file(
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'https://i.postimg.cc/BZCSwj2T/NSTB-Test-IMG-1472.jpg', 'NSTB.jpg')
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torch.hub.download_url_to_file(
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'https://i.postimg.cc/yYY1q7Tw/NSTC-Test-IMG-0118.jpg', 'NSTC.jpg')
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torch.hub.download_url_to_file(
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'https://i.postimg.cc/zD9ZQX6z/KCCA-Test-IMG-3555.jpg', 'KCCA.jpg')
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torch.hub.download_url_to_file(
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'https://i.postimg.cc/vZLPXP7L/KCCB-Test-IMG-3733.jpg', 'KCCB.jpg')
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torch.hub.download_url_to_file(
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'https://i.postimg.cc/BZFYqFmF/KCCC-Test-IMG-3892.jpg', 'KCCC.jpg')
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def detect_objects(image_path, selected_model):
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# Open the image file and resize it
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image = Image.open(image_path)
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resized_image = image.resize((1024, 768))
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#default
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# model_path = ('MvitHYF/v8mvitcocoaseed2024')
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# Load the model
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nstcurrentmodel = "NST Model"
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kcccurrentmodel = "KCC Model"
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currentmodel = str(selected_model)
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nstcurrentmodel = "NST Model"
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if currentmodel == nstcurrentmodel:
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print("this is nst model")
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#best of NST Model
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#model_path = ('code/runs/train45/best.pt')
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model_path = ('MvitHYF/v8mvitcocoaseed2024')
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elif currentmodel == kcccurrentmodel:
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print("this is kcc model")
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#model_path = ('code/runs/kcc/v8/train83/best.pt')
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model_path = ('MvitHYF/kccv8mvitcocoaseed2024')
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#model_path = ('code/runs/train45/best.pt')
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# model_path = ('MvitHYF/v8mvitcocoaseed2024')
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model = YOLO(model_path)
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# model = YOLO('MvitHYF/v8mvitcocoaseed2024')
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# Set model parameters
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model.overrides['conf'] = 'null' # NMS confidence threshold
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model.overrides['iou'] = 0.70 # NMS IoU threshold
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model.overrides['agnostic_nms'] = True # NMS class-agnostic
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model.overrides['max_det'] = 1000 # maximum number of detections per image
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# Perform inference
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results = model.predict(resized_image)
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#debug check count
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# print("see")
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# print(results)
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cls = results[0].boxes.cls
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# print(cls)
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strcls = str(cls)
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# print(type(strcls))
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# print(strcls)
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count_classa = strcls.count('0')
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# print('Count of classA:', count_classa)
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count_classb = strcls.count('1')
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# print('Count of classB', count_classb)
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count_classc = strcls.count('2')
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# print('Count of classC:', count_classc)
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intcount_classa = int(count_classa)
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intcount_classb = int(count_classb)
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intcount_classc = int(count_classc)
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total = intcount_classa + intcount_classb + intcount_classc
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# print("end see")
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# gr.Image(label="Pie Graph")
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# Format the output to print the counts
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output_counts = f"Totoal cocoa seeds: {total}\nClass A: {count_classa} seeds\nClass B: {count_classb} seeds\nClass C: {count_classc} seeds"
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# Render results
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render = render_result(model=model, image=resized_image, result=results[0])
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print("Selected model:", selected_model)
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#return render, output_counts, plotbar
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return render, output_counts, "You have selected the " + str(selected_model)
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#csspath = 'code/yolov8newultlt/gradio.css'
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with gr.Blocks(theme='ParityError/LimeFace') as demo:
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with gr.Row():
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with gr.Column():
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gr.Interface(fn=detect_objects,
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inputs=[gr.Image(type="filepath", label="Upload an Image"), gr.Dropdown(choices=["NST Model", "KCC Model"])],
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outputs=[gr.Image(type="filepath", label="Result"), gr.Textbox(label="Detection Counts"), gr.Textbox(label="Selected Model")],
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title="YOLOv8 Cocoa Seed Classification",
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description="Upload an image to detect objects using YOLO.",
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#html = gr.HTML(value="<p>This is another paragraph123.</p>"),
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examples = [["NSTA.jpg"],
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["NSTB.jpg"],
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["NSTC.jpg"],
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["KCCA.jpg"],
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["KCCB.jpg"],
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["KCCC.jpg"]],
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cache_examples = bool(False)
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#css=csspath,
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)
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# with gr.Row(): #original Column
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# with gr.Row(): #original Column
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# example = [[gr.Image(value="NSTA.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')],
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# [gr.Image(value="NSTB.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')],
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# [gr.Image(value="NSTC.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')],
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# [gr.Image(value="KCCA.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')],
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# [gr.Image(value="KCCB.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')],
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# [gr.Image(value="KCCC.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')]]
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with gr.Row():
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with gr.Row():
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# gr.HTML(value="<b>Class A</b> <p>Class A is the best from all 3 classes. It have the best of physical appreance eg. shape, size, texture</p>"),
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gr.HTML(value="<p> NSTA NSTB NTSC KCCA KCCB KCCC</p> <dl> <dt><b>Class A</b></dt> <dd>Class A is the best from all 3 classes. It have the best of physical appreance eg. shape, size, texture</dd> </dl> <dt><b>Class B</b></dt> <dd>Class B most of the cocoa seed have physical appreance similar to class A. <br> But the size must me smaller and texture is not smmoth as class A</dd> <dt><b>Class C</b></dt> <dd>Class C is the worst from all 3 classes. Its the smallest, rough texter and have a irregular shape </dd> </dl></dl>")
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if __name__ == "__main__":
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demo.queue().launch(share=True)
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