Update app.py

app.py

@@ -1,86 +1,30 @@
-import torch
-from PIL import Image
-import matplotlib.pyplot as plt
-import io
-from random import choice
 import gradio as gr
 from yolo import YOLO
 
 yolo = YOLO()
 
-# Colors for bounding boxes
-COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
-          "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
-          "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
-
-# Font dictionary for text annotations
-fdic = {
-    "family": "DejaVu Serif",
-    "style": "normal",
-    "size": 18,
-    "color": "yellow",
-    "weight": "bold"
-}
-
-def get_figure(in_pil_img, in_results):
-    """ Function to generate figure with bounding boxes and labels """
-    plt.figure(figsize=(16, 10))
-    plt.imshow(in_pil_img)
-    ax = plt.gca()
-
-    for score, label, box in zip(in_results["scores"], in_results["labels"], in_results["boxes"]):
-        selected_color = choice(COLORS)
-
-        box_int = [int(i.item()) for i in torch.round(box)]
-        x, y, w, h = box_int[0], box_int[1], box_int[2] - box_int[0], box_int[3] - box_int[1]
-
-        ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3, alpha=0.8))
-        ax.text(x, y)
-
-    plt.axis("off")
-    return plt.gcf()
-
 def predict(image):
-    try:
-        r_image = yolo.detect_image(image)
-        return r_image
-    except Exception as e:
-        return str(e)
-
-with gr.Blocks(title="MASFNet Object Detection",
-                    css=".gradio-container {background:lightyellow;}"
-               ) as demo:
-    #sample_index = gr.State([])
-
-    gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">MASFNet Object Detection</div>""")
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">a. Select an example by clicking a thumbnail below.</h4>""")
-    gr.HTML("""<h4 style="color:navy;">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(['img/1.png', 'img/2.png'], inputs=input_image)
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">3. Set a threshold value (default to 0.9)</h4>""")
-
-    # threshold = gr.Slider(0, 1.0, value=0.9, label='threshold')
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">4. Then, click "Infer" button to predict object instances. It will take about 10 seconds (yolos-tiny) or 20 seconds (yolos-small).</h4>""")
-
-    send_btn = gr.Button("Infer")
-    send_btn.click(fn=predict, inputs=[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://huggingface.co/docs/transformers/model_doc/yolos" target="_blank">Hugging Face Transformers - YOLOS</a>""")
-    gr.HTML("""</ul>""")
-
-
-#demo.queue()
-demo.launch(share=True)
+    r_image = yolo.detect_image(image)
+    return r_image
+
+
+title = "MASFNet: Multi-scale Adaptive Sampling Fusion Network for Object Detection in Adverse Weather"
+description = """
+The bot was trained to answer questions based on Rick and Morty dialogues. Ask Rick anything!
+<img src="https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px>
+"""
+
+article = "Check out [the original Rick and Morty Bot](https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot) that this demo is based off of."
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(type="file", label="Upload an Image"),
+    outputs=gr.outputs.Image("Prediction Result"),
+    title=title,
+    description=description,
+    article=article,
+    examples=[
+        ["img/1.png"],
+        ["img/2.png"]
+    ]
+).launch()