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.gitattributes

@@ -34,3 +34,5 @@ saved_model/**/* 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
+Blood[[:space:]]Cancer/blood_cancer.keras filter=lfs diff=lfs merge=lfs -text
+Blood[[:space:]]Cancer/test_imgs/benign.jpg filter=lfs diff=lfs merge=lfs -text

Blood Cancer/blood-cancer.py

@@ -0,0 +1,43 @@
+import numpy as np
+import cv2
+import streamlit as st
+from tensorflow.keras.models import load_model
+import warnings
+
+warnings.filterwarnings('ignore')
+st.title("Blood Cancer")
+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
+
+def pred(img,model):
+    img = np.expand_dims(img,axis=0)
+    pred = model.predict(img)
+    return np.argmax(pred)
+
+@st.cache_resource
+def load_model_():
+    model = load_model("blood_cancer.keras")
+    return model
+
+model = load_model_()
+test_imgs = ["test_imgs/benign.jpg",'test_imgs/early pre-b.jpg','test_imgs/pre-b.jpg','test_imgs/pro-b.jpg']
+for i in test_imgs:
+    img = load_img(i)
+    st.image(np.squeeze(img),caption="Blood Cell Image")
+    prediction = pred(img,model)
+    if prediction == 0:
+        st.markdown("#### This is a benign condition. No immediate medical action is needed.")
+
+    elif prediction == 1:
+        st.markdown("#### This is a pre-B malignant type. Medical consultation is highly recommended.")
+
+    elif prediction == 2:
+        st.markdown("#### This is a malignant cancer type. Please consult a doctor for further diagnosis.")
+
+    elif prediction == 3:
+        st.markdown("#### This is an early pre-B type. Regular monitoring and medical advice are advised.")

Blood Cancer/blood_cancer.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44e5fb43c9f2cfe99558e0a4846a75c4abd1db1b460a1f1baa969c13fb27248b
+size 62550779