Create app.py

app.py

@@ -0,0 +1,73 @@
+import torch
+from transformers import pipeline
+
+from PIL import Image
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import gradio as gr
+from random import choice
+import io
+
+
+model = pipeline(model="jaimin/ObjectDetect")
+
+
+
+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 = model(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
+
+
+input = gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)
+output = gr.outputs.Textbox(label="Captions")
+
+
+interface = gr.Interface(
+        fn=predict,
+        inputs = input,
+        outputs=output,
+    )
+interface.launch(debug=True)