Added code

app.py

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+import streamlit as st
+from PIL import Image
+from models import predict
+import numpy
+
+
+def load_image(image_file):
+    img = Image.open(image_file)
+    return img
+
+
+st.title("Cataract Image Classification")
+
+st.header('Enter the fundus image')
+st.subheader("KNN Model")
+
+image_file = st.file_uploader("Upload Images", type=["png", "jpg", "jpeg"])
+
+if image_file is not None:
+    img = load_image(image_file)
+    st.image(img, width=250)
+    open_cv_image = numpy.array(img)
+    label, prob = predict(open_cv_image)
+    st.write(f"Label : {label}")
+    st.write(f"Probability: {prob}")

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fa462398536a1ad24d9dcd31975a6ffd20e15bdfdc7d9bfde17fbb0bad05a7c4
+size 121565

models.py

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+import numpy as np
+import pandas as pd
+import cv2 as cv
+import streamlit as st
+import matplotlib.pyplot as plt
+from skimage.feature import graycomatrix, graycoprops
+import joblib
+
+indextable = ['dissimilarity', 'contrast',
+              'homogeneity', 'energy', 'correlation', 'Label']
+obj = {
+    0.0: "Normal",
+    1.0: "Cataract",
+    2.0: "Glaucoma",
+    3.0: 'Retina Disease'
+}
+width, height = 400, 400
+distance = 10
+teta = 90
+
+# Code to extract features from Image using Gray Level Co occurrence Image
+
+
+def get_feature(matrix, name):
+    feature = graycoprops(matrix, name)
+    result = np.average(feature)
+    return result
+
+
+def preprocessingImage(image):
+    test_img = cv.cvtColor(image, cv.COLOR_BGR2RGB)
+    test_img_gray = cv.cvtColor(test_img, cv.COLOR_RGB2GRAY)
+    test_img_thresh = cv.adaptiveThreshold(
+        test_img_gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, 11, 3)
+
+    cnts = cv.findContours(
+        test_img_thresh, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    cnts = cnts[0] if len(cnts) == 2 else cnts[1]
+    cnts = sorted(cnts, key=cv.contourArea, reverse=True)
+
+    for c in cnts:
+        x, y, w, h = cv.boundingRect(c)
+        test_img_ROI = test_img[y:y+h, x:x+w]
+        break
+
+    test_img_ROI_resize = cv.resize(test_img_ROI, (width, height))
+    test_img_ROI_resize_gray = cv.cvtColor(
+        test_img_ROI_resize, cv.COLOR_RGB2GRAY)
+
+    return test_img_ROI_resize_gray
+
+
+def extract(path):
+    data_eye = np.zeros((5, 1))
+
+    # path = cv.imread(path)
+    img = preprocessingImage(path)
+
+    glcm = graycomatrix(img, [distance], [teta],
+                        levels=256, symmetric=True, normed=True)
+
+    for i in range(len(indextable[:-1])):
+        features = []
+        feature = get_feature(glcm, indextable[i])
+        features.append(feature)
+        data_eye[i, 0] = features[0]
+    return pd.DataFrame(np.transpose(data_eye), columns=indextable[:-1])
+
+
+"""
+Return predicted class with its probability
+"""
+
+model = joblib.load("model.pkl")
+
+
+@st.cache
+def predict(path):
+    X = extract(path)
+    y = model.predict(X)[0]
+    prob = model.predict_proba(X)[0, int(y)]
+    return (obj[y], prob)

requirements.txt

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+numpy
+pandas
+opencv-python
+matplotlib
+scikit-learn
+scikit-image
+streamlit
+joblib
+pillow