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app.py

@@ -12,39 +12,23 @@ from PIL import Image
 IMG_SIZE = 300
 
 
-
-labelInfo = {
-    'lower-gi-tract anatomical-landmarks cecum': 0,
-    'lower-gi-tract anatomical-landmarks ileum': 1,
-    'lower-gi-tract anatomical-landmarks retroflex-rectum': 2,
-    'lower-gi-tract pathological-findings hemorrhoids': 3,
-    'lower-gi-tract pathological-findings polyps': 4,
-    'lower-gi-tract pathological-findings ulcerative-colitis-grade-0-1': 5,
-    'lower-gi-tract pathological-findings ulcerative-colitis-grade-1': 6,
-    'lower-gi-tract pathological-findings ulcerative-colitis-grade-1-2': 7,
-    'lower-gi-tract pathological-findings ulcerative-colitis-grade-2': 8,
-    'lower-gi-tract pathological-findings ulcerative-colitis-grade-2-3': 9,
-    'lower-gi-tract pathological-findings ulcerative-colitis-grade-3': 10,
-    'lower-gi-tract quality-of-mucosal-views bbps-0-1': 11,
-    'lower-gi-tract quality-of-mucosal-views bbps-2-3': 12,
-    'lower-gi-tract quality-of-mucosal-views impacted-stool': 13,
-    'lower-gi-tract therapeutic-interventions dyed-lifted-polyps': 14,
-    'lower-gi-tract therapeutic-interventions dyed-resection-margins': 15,
-    'upper-gi-tract anatomical-landmarks pylorus': 16,
-    'upper-gi-tract anatomical-landmarks retroflex-stomach': 17,
-    'upper-gi-tract anatomical-landmarks z-line': 18,
-    'upper-gi-tract pathological-findings barretts': 19,
-    'upper-gi-tract pathological-findings barretts-short-segment': 20,
-    'upper-gi-tract pathological-findings esophagitis-a': 21,
-    'upper-gi-tract pathological-findings esophagitis-b-d': 22
+labelOcular = {
+    'normal': 0,
+    'diabetes': 1,
+    'glaucoma': 2,
+    'cataract': 3,
+    'amd': 4,
+    'hypertension': 5,
+    'myopia': 6,
+    'other': 7
 }
 # Load the model from the H5 file
-model = tf.keras.models.load_model('model/Hyper.h5')
+model = tf.keras.models.load_model('model/Ocular.h5')
 
 # Define the prediction function
 def predict(img):
-    img_height = 300
-    img_width = 300
+    img_height = 224
+    img_width = 224
 
     # Convert the NumPy array to a PIL Image object
     pil_img = Image.fromarray(img)
@@ -61,7 +45,7 @@ def predict(img):
 
     predi = model.predict(x)
     accuracy_of_class = '{:.1f}'.format(predi[0][np.argmax(predi)] * 100) + "%"
-    classes = list(labelInfo.keys())[np.argmax(predi)]
+    classes = list(labelOcular.keys())[np.argmax(predi)]
     context = {
         'predictedLabel': classes,
         # 'y_class': y_class,
@@ -75,7 +59,7 @@ def predict(img):
 
 
 
-demo = gr.Interface(fn=predict, inputs="image", outputs="text" , examples=[["O1.jpg"],["O2.jpg"],["O3.jpg"]],)
+demo = gr.Interface(fn=predict, inputs="image", outputs="text" , examples=[["O1.jpg"],["O2.jpg"],["O2.jpg"]],)
 
-demo.launch(server_port=8000)
+demo.launch()