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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Chest[[:space:]]Xray/pneumonia.keras filter=lfs diff=lfs merge=lfs -text

Chest Xray/chest-xray.py

@@ -0,0 +1,38 @@
+import numpy as np
+import cv2
+import tensorflow as tf
+import streamlit as st
+from tensorflow.keras.models import load_model
+
+st.title("Pnuemonia")
+def load_img(path):
+    img = cv2.imread(path)
+    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+    img = cv2.resize(img,(224,224))
+    img = img.astype('float32')
+    img /= 255.0
+    return img
+
+imgs = ['person1676_virus_2892.jpeg','1-s2.0-S0263931909001811-gr3.jpg','43055_2020_296_Fig11_HTML.png','images.jpg']
+@st.cache_resource
+def load_cached_models():
+    model = load_model("pneumonia_vgg16.keras")
+    model1 = load_model("pnuemoniavgg16_1.keras")
+    model2 = load_model("pnuemoniavgg16_2.keras")
+    return model, model1, model2
+
+# Load the models once, cached in memory
+model, model1, model2 = load_cached_models()
+for img_p in imgs:
+    img = load_img(img_p)
+    img = np.expand_dims(img,axis=0)
+    pred = model.predict(img)
+    pred1 = model1.predict(img)
+    pred2 = model2.predict(img)
+    avg_pred = np.mean([pred, pred1, pred2])
+    st.subheader(img_p)
+    st.write("Prediction of model")
+    final_pred = round(avg_pred*100,2)
+    st.progress(int(round(avg_pred * 100)))
+    st.write(final_pred)
+    st.write("Pneumonia Detected" if avg_pred > .5 else "Your x-ray seems normal")

Chest Xray/pneumonia.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9555e9a3b0a40b5caba287d24c560076bb8c1a792d4bf01702b38ab79179793
+size 136022360