Update app.py

app.py

@@ -1,45 +1,31 @@
-
 import gradio as gr
-import tensorflow as tf 
-import tensorflow.keras 
-import matplotlib.pyplot as plt
-import cv2
+import tensorflow as tf
 import tensorflow_io as tfio
 import numpy as np
 
-loaded_model = tf.keras.models.load_model( 'kidney2.h5')
-
-def take_img(img):
-  
-  resize = tf.image.resize(img, (224,224))
-  gray = tfio.experimental.color.bgr_to_rgb(resize)
-  yhat = loaded_model.predict(np.expand_dims(gray/255, 0))
-  label_names = {
-  "1": "Cyst",
-  "2": "Normal",
-  "3": "Stone",
-  "4": "Tumor"    
-  }
-  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]
-  q= yhat[0][0]
-  w = yhat[0][1]
-  e = yhat[0][2]
-  r = yhat[0][3] 
-
-  q = str(q)
-  w = str(w)
-  e = str(e)
-  r = str(r)  
- 
-  return {'cryst' : q ,'Normal' : w ,'Stone' : e ,'Tumour' : r }
-
-
-
-image = gr.inputs.Image(shape=(224,224))
-
-label = gr.outputs.Label('ok')
-gr.Interface(fn=take_img, inputs=image, outputs="label",interpretation='default').launch(debug='True')
+loaded_model = tf.keras.models.load_model('kidney2.h5')
+
+def classify_kidney_image(img):
+    resize = tf.image.resize(img, (224, 224))
+    gray = tfio.experimental.color.bgr_to_rgb(resize)
+    yhat = loaded_model.predict(np.expand_dims(gray / 255, 0))
+    label_names = {
+        "1": "Cyst",
+        "2": "Normal",
+        "3": "Stone",
+        "4": "Tumor"
+    }
+    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]
+    q, w, e, r = yhat[0]
+
+    return {'Cyst': str(q), 'Normal': str(w), 'Stone': str(e), 'Tumor': str(r)}
+
+image = gr.inputs.Image(shape=(224, 224))
+label = gr.outputs.Label()
+
+app = gr.Interface(fn=classify_kidney_image, inputs=image, outputs=label, interpretation='default', title='Kidney Image Classifier')
+app.launch(debug=True)