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README.md

@@ -1,12 +1,6 @@
 ---
 title: Hyper
-emoji: 🏢
-colorFrom: indigo
-colorTo: gray
+app_file: app.py
 sdk: gradio
 sdk_version: 3.35.2
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+
+
+
+
+
+# Initial parameters for pretrained model
+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
+}
+# Load the model from the H5 file
+model = tf.keras.models.load_model('model/Hyper.h5')
+
+# Define the prediction function
+def predict(img):
+    img_height = 300
+    img_width = 300
+
+    # Convert the NumPy array to a PIL Image object
+    pil_img = Image.fromarray(img)
+
+    # Resize the image using the PIL Image object
+    pil_img = pil_img.resize((img_height, img_width))
+
+    # Convert the PIL Image object to a NumPy array
+    x = tf.keras.preprocessing.image.img_to_array(pil_img)
+
+    x = x.reshape(1, img_height, img_width, 3)
+    np.set_printoptions(formatter={'float': '{: 0.3f}'.format})
+
+
+    predi = model.predict(x)
+    accuracy_of_class = '{:.1f}'.format(predi[0][np.argmax(predi)] * 100) + "%"
+    classes = list(labelInfo.keys())[np.argmax(predi)]
+    context = {
+        'predictedLabel': classes,
+        # 'y_class': y_class,
+        # 'z_class': z_class,
+        'accuracy_of_class': accuracy_of_class
+    }
+
+
+   
+    return context
+
+
+
+demo = gr.Interface(fn=predict, inputs="image", outputs="text" , examples=[["O1.jpg"],["O2.jpg"],["O3.jpg"]],)
+
+demo.launch()
+

model/Hyper.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e9255d46684df0f3be60cdafc04b3dc2e7520041741d2265aec51b36ae1df9a1
+size 135418472

requirements.txt

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+tensorflow
+numpy
+pillow