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README.md

@@ -1,12 +1,6 @@
 ---
 title: Ocular
-emoji: 🏢
-colorFrom: indigo
-colorTo: pink
+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
+
+
+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/Ocular.h5')
+
+# Define the prediction function
+def predict(img):
+    img_height = 224
+    img_width = 224
+
+    # 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(labelOcular.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"],["O2.jpg"]],)
+
+demo.launch( server_port=8000)
+

model/Ocular.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:622eecde12d78f49e296cefb792541c610377f75e4b94bdaa84e857068081bdc
+size 202330160

requirements.txt

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+tensorflow
+numpy
+pillow