Upload ex.py

ex.py

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+from PIL import Image
+from prediction import prediction
+from prediction import pre_image
+from prediction import load_model
+from prediction import predict
+from prediction import annotate
+
+import gradio as gr
+import cv2
+import onnxruntime
+import matplotlib.pyplot as plt
+import fire
+
+model = load_model(model_path="best_re_final.onnx")
+
+# def greet(temperature):
+#     # salutation = "Good morning" if is_morning else "Good evening"
+#     # greeting = f"{salutation} {name}. It is {temperature} degrees today"
+#     celsius = (temperature - 32) * 5 / 9
+#     return  round(celsius, 2)
+
+
+
+
+def predict_gradio(image,Confidence,IOU):
+    
+    conf = Confidence /100
+    iou= IOU/100
+    input_I= pre_image(image, model[1]) #path and input shape is passed
+    predictions = predict(image, model[0], input_I, conf)  #image, ort_session, and input tensor is passed
+    annotatedImage = annotate(image, predictions[0], predictions[1], predictions[2],iou) #boxes, and scores are passed
+    annotatedImage =  cv2.cvtColor(annotatedImage, cv2.COLOR_BGR2RGB)
+
+    return annotatedImage
+
+
+
+demo = gr.Interface(  fn=predict_gradio,
+    inputs=[
+        "image",gr.Slider(0, 100),gr.Slider(0, 100)
+    ],
+    outputs="image",
+    title="Head Detection",
+    css=".gradio-container { background-color: grey; }"
+                    )
+demo.launch()