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Browse files- app.py +56 -0
- models/cataract/model_architecture_cataract.pkl +3 -0
- models/cataract/model_weights_cataract.pkl +3 -0
- requirements.txt +9 -0
app.py
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import os
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import pickle
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import numpy as np
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import tensorflow as tf
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from flask import Flask, render_template, request
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from PIL import Image
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import keras
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from keras.applications.vgg16 import preprocess_input
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from tensorflow.keras.applications.vgg16 import preprocess_input
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import gradio as gr
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def model(img):
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model_dir = 'cataract'
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class_names = ['Normal', 'Cataract'] # Cataract class labels
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channel='RGB'
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selected_model = 'cataract'
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architecture_path = os.path.join('models',model_dir, f'model_architecture_{selected_model}.pkl')
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weights_path = os.path.join('models',model_dir, f'model_weights_{selected_model}.pkl')
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with open(architecture_path, 'rb') as f:
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loaded_model_architecture = pickle.load(f)
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with open(weights_path, 'rb') as f:
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loaded_model_weights = pickle.load(f)
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# Create the model using the loaded architecture
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loaded_model = tf.keras.models.model_from_json(loaded_model_architecture)
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# Set the loaded weights to the model
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loaded_model.set_weights(loaded_model_weights)
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# Load and preprocess the image
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try:
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image = Image.open(img).convert(channel).resize((256, 256))
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except:
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print("ERROR")
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return "ERROR"
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x = np.array(image)
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x = np.expand_dims(x, axis=0)
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x = preprocess_input(x)
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# Make predictions
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predictions = loaded_model.predict(x)
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print(predictions)
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predicted_class_index = 1 if (predictions[0]>0.5) else 0
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print(predicted_class_index)
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predicted_class_label = class_names[predicted_class_index]
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return predicted_class_label
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cataract_app = gr.Interface(model,gr.Image())
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cataract_app.launch(share=True)
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models/cataract/model_architecture_cataract.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:fcfd80746e9a871e8198d36bd067c647449f7e914d382c5f37b287d14c783718
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size 20468
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models/cataract/model_weights_cataract.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:4c7803f31bae8513825e98ac5a13dc10dd764b1c0e276fd73bde04fba9be4b86
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size 80230146
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requirements.txt
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tensorflow==2.16.1
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keras==3.3.3
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numpy==1.26.4
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matplotlib==3.7.5
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pillow==9.5.0
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# glob
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flask
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# python==3.10
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gradio
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