initial commit

.gitignore

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+flagged/
+*.pt
+*.png
+*.jpg
+*.mp4
+*.mkv
+gradio_cached_examples/

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+from ultralytics import YOLO
+import requests
+import os
+
+model = YOLO('best.pt')
+
+def predict(image):
+    try:
+        image = np.array(image)
+        results = model(image)
+        result_image = results[0].plot()
+        return Image.fromarray(result_image)
+    except Exception as e:
+        print(f"Error during prediction: {e}")
+        return "Error"
+
+def load_image_from_gallery(images, index):
+    if images and 0 <= index < len(images):
+        image = images[index]
+        if isinstance(image, tuple):
+            image = image[0]
+        return image
+    return None
+
+def gallery_click_event(images, evt: gr.SelectData):
+    index = evt.index
+    selected_img = load_image_from_gallery(images, index)
+    return selected_img
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            selected_image = gr.Image(label="Selected Image from Gallery", type="pil")
+        
+        with gr.Column():
+            image_gallery = gr.Gallery(label="Image Gallery", elem_id="gallery", type="pil")
+        
+        with gr.Column():
+            result_image = gr.Image(label="Result Image", type="pil")
+
+    # Update selected image based on gallery click
+    image_gallery.select(
+        fn=gallery_click_event, 
+        inputs=image_gallery, 
+        outputs=selected_image
+    )
+    
+    # Predict and display the result image when the selected image changes
+    selected_image.change(
+        fn=predict, 
+        inputs=selected_image, 
+        outputs=result_image
+    )
+
+demo.launch()

requirements.txt

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+# YOLOv5 requirements
+# Usage: pip install -r requirements.txt
+
+# Base ------------------------------------------------------------------------
+gitpython>=3.1.30
+matplotlib>=3.3
+numpy>=1.23.5
+opencv-python>=4.1.1
+pillow>=10.3.0
+psutil  # system resources
+PyYAML>=5.3.1
+requests>=2.32.0
+scipy>=1.4.1
+thop>=0.1.1  # FLOPs computation
+torch>=1.8.0  # see https://pytorch.org/get-started/locally (recommended)
+torchvision>=0.9.0
+tqdm>=4.64.0
+ultralytics>=8.0.232
+# protobuf<=3.20.1  # https://github.com/ultralytics/yolov5/issues/8012
+
+# Logging ---------------------------------------------------------------------
+# tensorboard>=2.4.1
+# clearml>=1.2.0
+# comet
+
+# Plotting --------------------------------------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export ----------------------------------------------------------------------
+# coremltools>=6.0  # CoreML export
+# onnx>=1.10.0  # ONNX export
+# onnx-simplifier>=0.4.1  # ONNX simplifier
+# nvidia-pyindex  # TensorRT export
+# nvidia-tensorrt  # TensorRT export
+# scikit-learn<=1.1.2  # CoreML quantization
+# tensorflow>=2.4.0,<=2.13.1  # TF exports (-cpu, -aarch64, -macos)
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev>=2023.0  # OpenVINO export
+
+# Deploy ----------------------------------------------------------------------
+setuptools>=65.5.1 # Snyk vulnerability fix
+# tritonclient[all]~=2.24.0
+
+# Extras ----------------------------------------------------------------------
+# ipython  # interactive notebook
+# mss  # screenshots
+# albumentations>=1.0.3
+# pycocotools>=2.0.6  # COCO mAP
+wheel>=0.38.0 # not directly required, pinned by Snyk to avoid a vulnerability