Delete app.py

app.py

@@ -1,145 +0,0 @@
-
-
-import io
-import gradio as gr
-import matplotlib.pyplot as plt
-import requests, validators
-from sqlalchemy import true
-import torch
-import pathlib
-from PIL import Image
-import os
-
-from detecto import core, utils, visualize
-from detecto.core import Model as DetectoModel
-
-
-
-title = """<h1 id="title">AEYE INSPECTOR</h1>"""
-css = '''
-h1#title {
-  text-align: center;
-}
-'''
-COLORS = [
-    [0.000, 0.447, 0.741],
-    [0.850, 0.325, 0.098],
-    [0.929, 0.694, 0.125],
-    [0.494, 0.184, 0.556],
-    [0.466, 0.674, 0.188],
-    [0.301, 0.745, 0.933]
-]
-
-models = ["Detecto (Faster-RCNN)","YOLOv100"]
-urls = [#'http://fbbbb.ddns.net:4080/static/images/ai_img(1).jpg',
-        #'http://fbbbb.ddns.net:4080/static/images/ai_img(2).jpg',
-        #'http://fbbbb.ddns.net:4080/static/images/ai_img(3).jpg',
-        #'http://fbbbb.ddns.net:4080/static/images/ai_img(4).jpg',
-        #'http://fbbbb.ddns.net:4080/static/images/ai_img(5).jpg',
-        #'http://fbbbb.ddns.net:4080/static/images/ai_img(6).jpg',
-        #'http://fbbbb.ddns.net:4080/static/images/ai_img(7).jpg',
-        #'http://fbbbb.ddns.net:4080/static/images/ai_img(8).jpg',
-        #'http://fbbbb.ddns.net:4080/static/images/ai_img(9).jpg',
-        #'http://fbbbb.ddns.net:4080/static/images/ai_img(10).jpg'
-        ]
-
-
-def detect_objects(model_name,url_input,image_input,threshold):
-    if validators.url(url_input):
-        image = Image.open(requests.get(url_input, stream=True).raw)
-    elif image_input:
-        image = image_input
-        
-    if  'Detecto' in model_name:
-        model = DetectoModel(['heltmet_safe','face_mask','safety_vest','safety_belts','safety_shoes'])
-        model.load('ai_30ep.pth',['heltmet_safe','face_mask','safety_vest','safety_belts','safety_shoes'])
-
-        print("OK")
-        labels, boxes, scores = model.predict(image)
-        viz_img = visualize_prediction(image, labels, boxes, scores, threshold)
-        
-        print(labels)
-        #print(boxes)
-        print(scores)
-        
-        return viz_img
-
-def visualize_prediction(pil_img, labels, boxes, scores, threshold=0.7):
-    keeps = scores > threshold
-    print(keeps)
-    
-    boxess = boxes[keeps].tolist()
-    print(boxess)
-    
-    #labelss = labels[keep]
-    #print(labelss)
-
-    plt.figure(figsize=(16, 10))
-    plt.imshow(pil_img)
-    ax = plt.gca()
-    colors = COLORS * 100
-    for idx, keep in enumerate(keeps):
-      if keep:
-        (xmin, ymin, xmax, ymax) = zip(boxess[idx])
-        print(xmin[0])
-        print(ymin[0])
-        ax.add_patch(plt.Rectangle((xmin[0], ymin[0]), xmax[0] - xmin[0], ymax[0] - ymin[0], fill=False, color=colors[idx], linewidth=3))
-        ax.text(xmin[0], ymin[0], f'{labels[idx]}: {scores[idx]:0.2f}', fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
-        
-    plt.axis("off")
-    return fig2img(plt.gcf())
-        
-def set_example_image(example: list) -> dict:
-    return gr.Image.update(value=example[0])
-    
-def set_example_url(example: list) -> dict:
-    return gr.Textbox.update(value=example[0])   
-    
-def fig2img(fig):
-    buf = io.BytesIO()
-    fig.savefig(buf)
-    buf.seek(0)
-    img = Image.open(buf)
-    return img     
-
-
-
-app = gr.Blocks(css=css)
-
-with app:
-    gr.Markdown(title)
-    options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True)
-    slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.7,label='Prediction Threshold')
-    
-    with gr.Tabs():
-        with gr.TabItem('Image URL'):
-            with gr.Row():
-                url_input = gr.Textbox(lines=2,label='Enter valid image URL here..')
-                img_output_from_url = gr.Image(shape=(650,650))
-                
-            with gr.Row():
-                example_url = gr.Dataset(components=[url_input],samples=[[str(url)] for url in urls])
-            
-            url_but = gr.Button('Detect')
-
-     
-        with gr.TabItem('Image Upload'):
-            with gr.Row():
-                img_input = gr.Image(type='pil')
-                img_output_from_upload= gr.Image(shape=(650,650))
-                
-            with gr.Row(): 
-                example_images = gr.Dataset(components=[img_input],
-                                            samples=[[path.as_posix()]
-                                                     for path in sorted(pathlib.Path('images').rglob('*.JPG'))])
-                
-            img_but = gr.Button('Detect')
-        
-    
-    url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_url,queue=True)
-    img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_upload,queue=True)
-    example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])
-    example_url.click(fn=set_example_url,inputs=[example_url],outputs=[url_input])
-    
-   
-app.launch(enable_queue=True, server_name='0.0.0.0', show_error=True)