Delete app.py

app.py

@@ -1,236 +0,0 @@
-
-import torch
-from transformers import pipeline
-
-from PIL import Image
-
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-
-from random import choice
-import io
-
-detector50 = pipeline(model="facebook/detr-resnet-50")
-
-detector101 = pipeline(model="facebook/detr-resnet-101")
-
-
-import gradio as gr
-
-COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
-            "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
-            "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
-
-fdic = {
-    "family" : "Impact",
-    "style" : "italic",
-    "size" : 15,
-    "color" : "yellow",
-    "weight" : "bold"
-}
-
-
-def get_figure(in_pil_img, in_results):
-    plt.figure(figsize=(16, 10))
-    plt.imshow(in_pil_img)
-    #pyplot.gcf()
-    ax = plt.gca()
-
-    for prediction in in_results:
-        selected_color = choice(COLORS)
-
-        x, y = prediction['box']['xmin'], prediction['box']['ymin'],
-        w, h = prediction['box']['xmax'] - prediction['box']['xmin'], prediction['box']['ymax'] - prediction['box']['ymin']
-
-        ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3))
-        ax.text(x, y, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontdict=fdic)
-
-    plt.axis("off")
-
-    return plt.gcf()
-
-
-def infer(model, in_pil_img):
-
-    results = None
-    if model == "detr-resnet-101":
-        results = detector101(in_pil_img)
-    else:
-        results = detector50(in_pil_img)
-
-    figure = get_figure(in_pil_img, results)
-
-    buf = io.BytesIO()
-    figure.savefig(buf, bbox_inches='tight')
-    buf.seek(0)
-    output_pil_img = Image.open(buf)
-
-    return output_pil_img
-
-
-with gr.Blocks(title="DETR Object Detection by orYx Models") as demo:
-    gr.HTML("""
-    <style>
-    .logo {
-        position: absolute;
-        top: 10px;
-        right: 10px;
-        width: 100px; /* Adjust the width of the logo as needed */
-        height: auto;
-    }
-    </style>
-    <div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">DETR Object Detection</div>
-    <img class="logo" src="https://huggingface.co/spaces/orYx-models/object-detection-facebook-ResNets/blob/main/oryx_logo%20(2).png" alt="Logo">
-    <h4 style="color:navy;">1. Select a model.</h4>
-    """)
-
-    model = gr.Radio(["detr-resnet-50", "detr-resnet-101"], value="detr-resnet-50", label="Model name")
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">2-a. Select an example by clicking a thumbnail below.</h4>""")
-    gr.HTML("""<h4 style="color:navy;">2-b. Or upload an image by clicking on the canvas.</h4>""")
-
-    with gr.Row():
-        input_image = gr.Image(label="Input image", type="pil")
-        output_image = gr.Image(label="Output image with predicted instances", type="pil")
-
-    gr.Examples(['https://huggingface.co/spaces/orYx-models/object-detection-facebook-ResNets/blob/main/traffic.jpg',
-                 'https://huggingface.co/spaces/orYx-models/object-detection-facebook-ResNets/blob/main/flyover.jpg'
-                
-import torch
-from transformers import pipeline
-
-from PIL import Image
-
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-
-from random import choice
-import io
-
-detector50 = pipeline(model="facebook/detr-resnet-50")
-
-detector101 = pipeline(model="facebook/detr-resnet-101")
-
-
-import gradio as gr
-
-COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
-            "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
-            "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
-
-fdic = {
-    "family" : "Impact",
-    "style" : "italic",
-    "size" : 15,
-    "color" : "yellow",
-    "weight" : "bold"
-}
-
-
-def get_figure(in_pil_img, in_results):
-    plt.figure(figsize=(16, 10))
-    plt.imshow(in_pil_img)
-    #pyplot.gcf()
-    ax = plt.gca()
-
-    for prediction in in_results:
-        selected_color = choice(COLORS)
-
-        x, y = prediction['box']['xmin'], prediction['box']['ymin'],
-        w, h = prediction['box']['xmax'] - prediction['box']['xmin'], prediction['box']['ymax'] - prediction['box']['ymin']
-
-        ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3))
-        ax.text(x, y, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontdict=fdic)
-
-    plt.axis("off")
-
-    return plt.gcf()
-
-
-def infer(model, in_pil_img):
-
-    results = None
-    if model == "detr-resnet-101":
-        results = detector101(in_pil_img)
-    else:
-        results = detector50(in_pil_img)
-
-    figure = get_figure(in_pil_img, results)
-
-    buf = io.BytesIO()
-    figure.savefig(buf, bbox_inches='tight')
-    buf.seek(0)
-    output_pil_img = Image.open(buf)
-
-    return output_pil_img
-
-
-with gr.Blocks(title= "DETR Object Detection by orYx Models") as demo:
-    gr.HTML("""
-    <style>
-    .logo {
-        position: absolute;
-        top: 10px;
-        right: 10px;
-        width: 100px; /* Adjust the width of the logo as needed */
-        height: auto;
-    }
-    </style>
-    <div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">DETR Object Detection</div>
-    <img class="logo" src="https://huggingface.co/spaces/orYx-models/object-detection-facebook-ResNets/blob/main/oryx_logo%20(2).png" alt="Logo">
-    <h4 style="color:navy;">1. Select a model.</h4>
-    """)
-
-    model = gr.Radio(["detr-resnet-50", "detr-resnet-101"], value="detr-resnet-50", label="Model name")
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">Please upload an image by clicking on the canvas. </h4>""")
-
-    with gr.Row():
-        input_image = gr.Image(label="Input image", type="pil")
-        output_image = gr.Image(label="Output image with predicted instances", type="pil")
-
-    gr.Examples(['https://huggingface.co/spaces/orYx-models/object-detection-facebook-ResNets/blob/main/traffic.jpg',
-                 'https://huggingface.co/spaces/orYx-models/object-detection-facebook-ResNets/blob/main/flyover.jpg',
-                 https://huggingface.co/spaces/orYx-models/object-detection-facebook-ResNets/resolve/main/trees_traffic.jpg'
-                 'https://huggingface.co/spaces/orYx-models/object-detection-facebook-ResNets/resolve/main/Saudi_traffic.jpg'], inputs=input_image)
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">3. Then, click "Infer" button to predict object instances. It will take about 10 seconds (on cpu)</h4>""")
-
-    send_btn = gr.Button("Infer")
-    send_btn.click(fn=infer, inputs=[model, input_image], outputs=[output_image])
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">Reference</h4>""")
-    gr.HTML("""<ul>""")
-    gr.HTML("""<li><a href="https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_attention.ipynb" target="_blank">Hands-on tutorial for DETR</a>""")
-    gr.HTML("""</ul>""")
-
-
-#demo.queue()
-demo.launch(debug=True)
-
-
-### EOF ###
-], inputs=input_image)
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">3. Then, click "Infer" button to predict object instances. It will take about 10 seconds (on cpu)</h4>""")
-
-    send_btn = gr.Button("Infer")
-    send_btn.click(fn=infer, inputs=[model, input_image], outputs=[output_image])
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">Reference</h4>""")
-    gr.HTML("""<ul>""")
-    gr.HTML("""<li><a href="https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_attention.ipynb" target="_blank">Hands-on tutorial for DETR</a>""")
-    gr.HTML("""</ul>""")
-
-
-#demo.queue()
-demo.launch(debug=True)
-
-
-### EOF ###