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README.md

@@ -1,12 +1,13 @@
 ---
-title: TestforajA
-emoji: 🌍
-colorFrom: blue
-colorTo: green
+title: Yolov8newultlt
+emoji: 🌖
+colorFrom: red
+colorTo: blue
 sdk: gradio
-sdk_version: 5.0.2
+sdk_version: 4.22.0
 app_file: app.py
 pinned: false
+models: [MvitHYF/v8mvitcocoaseed2024]
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+#app7.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/g2xGJ4Qs/NSTA-Test-IMG-3276.jpg', 'NSTA.jpg')
+torch.hub.download_url_to_file(
+    'https://i.postimg.cc/BZCSwj2T/NSTB-Test-IMG-1472.jpg', 'NSTB.jpg')
+torch.hub.download_url_to_file(
+    'https://i.postimg.cc/yYY1q7Tw/NSTC-Test-IMG-0118.jpg', 'NSTC.jpg')
+torch.hub.download_url_to_file(
+    'https://i.postimg.cc/zD9ZQX6z/KCCA-Test-IMG-3555.jpg', 'KCCA.jpg')
+torch.hub.download_url_to_file(
+    'https://i.postimg.cc/vZLPXP7L/KCCB-Test-IMG-3733.jpg', 'KCCB.jpg')
+torch.hub.download_url_to_file(
+    'https://i.postimg.cc/BZFYqFmF/KCCC-Test-IMG-3892.jpg', 'KCCC.jpg')
+
+def detect_objects(image_path, selected_model):
+    # Open the image file and resize it
+    image = Image.open(image_path)
+    resized_image = image.resize((1024, 768))
+
+    #default
+    # model_path = ('MvitHYF/v8mvitcocoaseed2024')
+    
+    # Load the model
+    nstcurrentmodel = "NST Model"
+    kcccurrentmodel = "KCC Model"
+    currentmodel = str(selected_model)
+    nstcurrentmodel = "NST Model"
+    if currentmodel == nstcurrentmodel:
+        print("this is nst model")
+        #best of NST Model
+        #model_path = ('code/runs/train45/best.pt')
+        model_path = ('MvitHYF/v8mvitcocoaseed2024')
+    elif currentmodel == kcccurrentmodel:
+        print("this is kcc model")
+        #model_path = ('code/runs/kcc/v8/train83/best.pt')
+        model_path = ('MvitHYF/kccv8mvitcocoaseed2024')
+
+    #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'] = 'null'  # NMS confidence threshold
+    model.overrides['iou'] = 0.70  # NMS IoU threshold
+    model.overrides['agnostic_nms'] = True  # 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")
+
+    # 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])
+
+    print("Selected model:", selected_model)
+
+    #return render, output_counts, plotbar
+    return render, output_counts, "You have selected the " + str(selected_model)
+
+#csspath = 'code/yolov8newultlt/gradio.css'
+
+with gr.Blocks(theme='ParityError/LimeFace') as demo:
+
+    with gr.Row():
+        with gr.Column():
+             gr.Interface(fn=detect_objects,
+                          inputs=[gr.Image(type="filepath", label="Upload an Image"), gr.Dropdown(choices=["NST Model", "KCC Model"])],
+                          outputs=[gr.Image(type="filepath", label="Result"), gr.Textbox(label="Detection Counts"), gr.Textbox(label="Selected Model")],
+                          title="YOLOv8 Cocoa Seed Classification",
+                          description="Upload an image to detect objects using YOLO.",
+                          #html = gr.HTML(value="<p>This is another paragraph123.</p>"),
+                          examples = [["NSTA.jpg"],
+                                      ["NSTB.jpg"],
+                                      ["NSTC.jpg"],
+                                      ["KCCA.jpg"],
+                                      ["KCCB.jpg"],
+                                      ["KCCC.jpg"]],
+                          cache_examples = bool(False)
+                          #css=csspath,
+                          )
+             
+    # with gr.Row(): #original Column
+    #     with gr.Row(): #original Column
+    #         example = [[gr.Image(value="NSTA.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')],
+    #                    [gr.Image(value="NSTB.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')],
+    #                    [gr.Image(value="NSTC.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')],
+    #                    [gr.Image(value="KCCA.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')],
+    #                    [gr.Image(value="KCCB.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')],
+    #                    [gr.Image(value="KCCC.jpg", interactive = bool(True)), gr.Markdown(value='**label 5**')]]
+    
+
+    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="<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; NSTA &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; NSTB &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; NTSC &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; KCCA &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; KCCB &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 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>")
+         
+if __name__ == "__main__":
+    demo.queue().launch(share=True) 

gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

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
+numpy
+Pillow
+torch
+torchvision
+ultralyticsplus