init

app.py

@@ -1,7 +1,34 @@
 import gradio as gr
+import cv2
 
-def greet(name):
-    return "Hello " + name + "!!"
+def predict_fn(img_path):
+    #Read and tranform input image
+    # preds=model.predict(img_path).json()
+    og_img=cv2.imread(img_path)
+    # img=binary_cv2(og_img,preds)
+    # outputs = kp_predictor(img)  # format is documented at https://detectron2.readthedocs.io/tutorials/models.html#model-output-format
+    # print("outputs==".format(outputs["instances"].to("cpu")))
+    # p=np.asarray(outputs["instances"].to("cpu").pred_keypoints, dtype='float32')[0].reshape(-1)
+    # pairss=convert_to_pairs(p)
+    # segm=segm_imf(preds,og_img)
+    # output=visualize(segm,pairss)
+    return og_img
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text",  _api_mode=True)
-iface.launch()
+
+
+inputs_image = [
+    gr.components.Image(type="filepath", label="Upload an XRay Image of the Pelvis"),
+]
+outputs_image = [
+    gr.components.Image(type="numpy", label="Output Image")
+] 
+
+
+
+gr.Interface(
+    predict_fn,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title="Coordinates of the Landmarks",
+    _api_mode=True
+).launch()