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app.py

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+#app6.py
+import gradio as gr
+import torch
+from PIL import Image
+from torchvision import transforms
+from ultralyticsplus import YOLO, render_result
+# import matplotlib.pyplot as plt
+import numpy as np
+
+torch.hub.download_url_to_file(
+    'https://i.postimg.cc/Y0V4KwMf/NSTA-Test-IMG-3276.jpg', 'A.jpg')
+torch.hub.download_url_to_file(
+    'https://i.postimg.cc/pTshCFSS/NSTB-Test-IMG-1472.jpg', 'B.jpg')
+torch.hub.download_url_to_file(
+    'https://i.postimg.cc/mkhc5rfg/NSTC-Test-IMG-0118.jpg', 'C.jpg')
+
+def detect_objects(image_path):
+    # Open the image file and resize it
+    image = Image.open(image_path)
+    resized_image = image.resize((1024, 768))
+
+    # Load the model
+    # model_path = ('code/runs/train45/best.pt')
+    model_path = ('MvitHYF/v8mvitcocoaseed2024')
+    model = YOLO(model_path)
+    # model = YOLO('MvitHYF/v8mvitcocoaseed2024')
+
+    # Set model parameters
+    model.overrides['conf'] = 0.35  # NMS confidence threshold
+    model.overrides['iou'] = 0.45  # NMS IoU threshold
+    model.overrides['agnostic_nms'] = False  # NMS class-agnostic
+    model.overrides['max_det'] = 1000  # maximum number of detections per image
+    
+    # Perform inference
+    results = model.predict(resized_image)
+
+    #debug check count
+    # print("see")
+    # print(results)
+    cls = results[0].boxes.cls
+    # print(cls)
+    strcls = str(cls)
+    # print(type(strcls))
+    # print(strcls)
+    count_classa = strcls.count('0')
+    # print('Count of classA:', count_classa)
+    count_classb = strcls.count('1')
+    # print('Count of classB', count_classb)
+    count_classc = strcls.count('2')
+    # print('Count of classC:', count_classc)
+    intcount_classa = int(count_classa)
+    intcount_classb = int(count_classb)
+    intcount_classc = int(count_classc)
+    total = intcount_classa + intcount_classb + intcount_classc 
+    # print("end see")
+
+    # class_counts = {'A': 0, 'B': 0, 'C': 0}
+    
+    # for result in results:
+    #     # Example to access class_id, adapt based on your results structure
+    #     class_id = result[-1]
+    #     if class_id == 0:  # if the class_id corresponds to class A
+    #         class_counts['A'] += 1
+    #     elif class_id == 1:  # if the class_id corresponds to class B
+    #         class_counts['B'] += 1
+    #     elif class_id == 2:  # if the class_id corresponds to class C
+    #         class_counts['C'] += 1
+
+    #plot graph
+    # plot = np.array([intcount_classa, intcount_classb, intcount_classc])
+    # piegraph = plt.pie(plot)
+
+    # x = np.array(["A", "B", "C"])
+    # y = np.array([intcount_classa, intcount_classb, intcount_classc])
+    # plotbar = plt.bar(x,y)
+
+    gr.Image(label="Pie Graph")
+    # Format the output to print the counts
+    output_counts = f"Totoal cocoa seeds: {total}\nClass A: {count_classa} seeds\nClass B: {count_classb} seeds\nClass C: {count_classc} seeds"
+
+    # Render results
+    render = render_result(model=model, image=resized_image, result=results[0])
+    #return render, output_counts, plotbar
+    return render, output_counts
+
+#csspath = 'code/yolov8newultlt/gradio.css'
+
+with gr.Blocks(theme='ParityError/LimeFace') as demo:
+    with gr.Row(): #original Column
+        with gr.Row(): #original Column
+            example = [['A.jpg'],
+                       ['B.jpg'],
+                       ['C.jpg']]
+    
+    with gr.Row():
+        with gr.Column():
+             gr.Interface(fn=detect_objects,
+                          inputs=gr.Image(type="filepath", label="Upload an Image"),
+                          outputs=[gr.Image(type="filepath", label="Result"), gr.Textbox(label="Detection Counts")],
+                          title="YOLOv8 Cocoas Seed Classification",
+                          description="Upload an image to detect objects using YOLO.",
+                          #html = gr.HTML(value="<p>This is another paragraph123.</p>"),
+                          examples = example,
+                          #css=csspath,
+                          )
+    with gr.Row():
+          with gr.Row():
+            #   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>"),
+            gr.HTML(value="<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>")
+         
+if __name__ == "__main__":
+    demo.queue().launch(share=True) 
+
+# def load_css():
+#     with open(csspath, 'r') as file:
+#         css_content = file.read()
+#     return css_content
+
+
+# Create the Gradio interface
+# iface = gr.Interface(fn=detect_objects,
+#                      inputs=gr.Image(type="filepath", label="Upload an Image"),
+#                      outputs=gr.Image(type="filepath", label="Result"),
+#                      description="Upload an image to detect objects using YOLO.",
+#                      title="YOLOv8 Cocoa Seed Classification",
+#                      examples=example,
+#                      theme='ParityError/LimeFace',
+#                      #css=load_css()
+#                      )
+
+# Launch the interface
+# iface.launch()
+# demo.queue().launch()

gradio.css

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+/* gradio.css */
+
+/* Target the images within the gradio app */
+img {
+    width: 1024px;  /* Set width directly */
+    height: 768px;  /* Set height directly */
+    margin: 10px;   /* Optional: add some space around the images */
+}

requirements.txt

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+gradio==4.22.0
+numpy==1.26.4
+Pillow==10.2.0
+torch==2.0.1
+torchvision==0.15.2
+ultralyticsplus==0.0.29