Rename app (2).py to app.py

app (2).py

@@ -1,36 +0,0 @@
-
-import gradio as gr
-import tensorflow as tf 
-import tensorflow.keras 
-import matplotlib.pyplot as plt
-import cv2
-import tensorflow_io as tfio
-import numpy as np
-
-loaded_model = tf.keras.models.load_model( 'brain1.h5')
-
-def take_img(img):
-  
-  resize = tf.image.resize(img, (128,128))
-  gray = tfio.experimental.color.bgr_to_rgb(resize)
-  yhat = loaded_model.predict(np.expand_dims(gray/255, 0))
-  label_names = {
-  "1": "Tumor",
-  "2": "Normal"}
-  classes_x=np.argmax(yhat,axis=1)
-  a = classes_x[0]
-  input_value = a + 1
-  input_str = str(input_value)
-  predicted_label = label_names[input_str]
-  tumor = yhat[0][0]
-  tumor = str(tumor)
-  normal = yhat[0][1]
-  normal = str(normal)
-  return {'Tumour': tumor, 'Normal':normal}
-
-
-
-image = gr.inputs.Image(shape=(128,128))
-
-label = gr.outputs.Label('ok')
-gr.Interface(fn=take_img, inputs=image, outputs="label",interpretation='default').launch(debug='True')

app.py

@@ -0,0 +1,22 @@
+
+import gradio as gr
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+model = YOLO('best (6).pt')
+
+def predict_image(img): 
+        
+  model = YOLO('best (6).pt')
+  result = model.predict(img)
+  res_plotted = result[0].plot()
+  #cv2.imshow( res_plotted)
+  return res_plotted
+
+
+
+image = gr.inputs.Image()
+
+label = gr.outputs.Label('ok')
+gr.Interface(fn=predict_image, inputs=image, outputs=image).launch(debug='True')