Create app.py

app.py

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+import tensorflow as tf
+import cv2
+import numpy as np
+from glob import glob
+# from models import Yolov4
+import gradio as gr
+# model = Yolov4(weight_path="best.pt", class_name_path='coco_classes.txt')
+
+
+from ultralytics import YOLO
+
+# Load a model
+model = YOLO("best.pt")  # load a custom model
+
+# Predict with the model
+# results = model("image.jpg", save = True)  # predict on an image
+
+
+def gradio_wrapper(img):
+    global model
+    #print(np.shape(img))
+    results = model.predict(img)  # predict on an image
+    try:
+        if max(results[0].boxes.cls) == 0:
+            text = "Man"
+        if max(results[0].boxes.cls) == 1:
+            text = "Women"
+    except:
+        pass
+        
+    return cv2.putText(img, text,(00, 185), cv2.FONT_HERSHEY_SIMPLEX, 1, 
+                  (0, 0, 255),  2, cv2.LINE_AA, False)
+    # return results
+  
+demo = gr.Interface(
+    gradio_wrapper,
+    #gr.Image(source="webcam", streaming=True, flip=True),
+    gr.Image(source="webcam", streaming=True),
+    "image",
+    live=True
+)
+
+demo.launch()