Delete app.py

app.py

@@ -1,88 +0,0 @@
-import gradio as gr
-
-from timeit import default_timer as timer
-from typing import Tuple , Dict
-import tensorflow as tf 
-import numpy as np
-
-from PIL import Image
-import os
-
-# 1.Import and class names setup
-class_names = ['CNV','DME','DRUSEN','NORMAL']
-
-
-# 2. Model annd transforms prepration
-# model = tf.keras.models.load_model(
-#     'oct_classification_final_model_lg.keras', custom_objects=None, compile=True, safe_mode=True
-# )
-model = tf.keras.models.load_model(
-    'oct_classification_final_model_lg.keras', custom_objects=None, compile=True, safe_mode=False
-)
-
-
-# Load save weights
-
-# 3.prediction function (predict())
-
-def load_and_prep_imgg(img : Image.Image, img_shape=224, scale=True):
-    # if not isinstance(filename, str):
-    #     raise ValueError("The filename must be a string representing the file path.")
-    # img = tf.io.read_file(filename)
-    # img = tf.io.decode_image(img, channels=3)
-    # img = tf.image.resize(img, size=[img_shape, img_shape])
-    # if scale:
-    #     return img / 255
-    # else:
-    #     return img
-    img = img.resize((img_shape, img_shape))
-    img = np.array(img)
-    if img.shape[-1] == 1:  # If the image is grayscale
-        img = np.stack([img] * 3, axis=-1)
-    img = tf.convert_to_tensor(img, dtype=tf.float32)
-    if scale:
-        return img / 255.0
-    else:
-        return img
-        
-def predict(img) -> Tuple[Dict,float] :
-
-  start_time = timer()
-
-  image =  load_and_prep_imgg(img)
-  #image = Image.open(image)  
-
-  pred_img = model.predict(tf.expand_dims(image, axis=0))
-  pred_class = class_names[pred_img.argmax()]
-  print(f"Predicted brain tumor is: {pred_class} with probability: {pred_img.max():.2f}")
-
-  
-
-  
-  end_time = timer()
-  pred_time = round(end_time - start_time , 4)
-
-  return pred_class , pred_time
-
-### 4. Gradio app - our Gradio interface + launch command
-
-title = 'Macular Disease Classification'
-description = 'Feature Extraction VGG model to classify Macular Diseases by OCT'
-article = 'Created with TensorFlow Model Deployment'
-# Create example list
-
-example_list = [['examples/'+ example] for example in os.listdir('examples')]
-example_list
-
-# create a gradio demo
-demo = gr.Interface(fn=predict ,
-                    inputs=gr.Image(type='pil'),
-                    outputs=[gr.Label(num_top_classes = 3 , label= 'prediction'),
-                            gr.Number(label= 'Prediction time (s)')],
-                    examples = example_list,
-                    title = title,
-                    description = description,
-                    article= article)
-
-# Launch the demo
-demo.launch(debug= False)