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app.py

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+import io
+import os
+import numpy as np
+import streamlit as st
+import requests
+from PIL import Image
+from model import classify
+import cv2
+
+@st.cache(allow_output_mutation=True)
+# def get_model():
+#     return bone_frac()
+
+# pred_model = get_model()
+# pred_model=bone_frac()
+
+def predict():
+    c=classify('tmp.jpg')
+    st.markdown('#### Predicted Captions:')
+    st.write(c)
+
+st.title('Image Captioner')
+img_url = st.text_input(label='Enter Image URL')
+
+if (img_url != "") and (img_url != None):
+    img = Image.open(requests.get(img_url, stream=True).raw)
+    img = img.convert('RGB')
+    st.image(img)
+    img.save('tmp.jpg')
+    predict()
+    os.remove('tmp.jpg')
+
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+# st.markdown('<center style="opacity: 70%">OR</center>', unsafe_allow_html=True)
+img_upload = st.file_uploader(label='Upload Image', type=['jpg', 'png', 'jpeg'])
+
+if img_upload != None:
+    img = img_upload.read()
+    img = Image.open(io.BytesIO(img))
+    img = img.convert('RGB')
+    img=np.asarray(img)
+    print(img)
+    # img=cv2.imread(img)
+    # img.save('tmp.jpg')
+    st.image(img)
+    c=classify(img)
+    st.markdown('#### Predicted Captions:')
+    st.write(c)
+    # predict()
+    # os.remove('tmp.jpg')

model.py

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+import tensorflow as tf
+import cv2
+import numpy as np
+
+
+def classify(img):
+    im = img
+    lt = ["other","Bone","Brain","eye","kidney","chest","skin"] 
+    im = cv2.resize(im,(52,52))
+    model = tf.keras.models.load_model("Models/all-in-one.h5")
+    result = model.predict(np.array([im]))
+    a = np.argmax(result)
+    c=""
+    if a==0:
+        return "Enter the medical Image"
+    if a==1:
+        c = bone_net(img)
+    if a==2:
+        c = brain_net(img)
+    if a==3:
+        c = Eye_net(img)
+    if a==4:
+        c = kidney_net(img)
+    if a==5:
+        c = chest_net(img)
+    if a==6:
+        c = skin_net(img)
+    return c
+
+
+
+def bone_net(img):
+    img = cv2.resize(img,(224,224))
+    model = tf.keras.models.load_model("Models/Fracture_detection.h5")
+    result = model.predict(np.array([img]))
+    op=""
+    if result[0]<0.5:
+        op="Fracture"   
+    else:
+        op="Normal"
+    return op
+
+def brain_net(img):
+    lt = ['pituitary', 'notumor', 'meningioma', 'glioma']
+    img = cv2.resize(img,(224,224))
+    model = tf.keras.models.load_model("Models/model_brain_tumur.h5")
+    result = model.predict(np.array([img]))
+    ans = np.argmax(result)
+    return lt[ans]
+
+def chest_net(img):
+    lt = ['PNEUMONIA', 'NORMAL']
+    img = cv2.resize(img,(224,224))
+    model = tf.keras.models.load_model("Models/chest_cls_model.h5")
+    result = model.predict(np.array([img]))
+    ans = np.argmax(result)
+    return lt[ans]
+
+def Eye_net(img):
+    lt = ['glaucoma', 'normal', 'diabetic_retinopathy', 'cataract']
+    img = cv2.resize(img,(224,224))
+    model = tf.keras.models.load_model("Models/Eye_diseases.h5")
+    result = model.predict(np.array([img]))
+    ans = np.argmax(result)
+    return lt[ans]
+
+def kidney_net(img):
+    lt = ['Cyst', 'Tumor', 'Stone', 'Normal']
+    img = cv2.resize(img,(224,224))
+    model = tf.keras.models.load_model("Models/kidney_stone.h5")
+    result = model.predict(np.array([img]))
+    ans = np.argmax(result)
+    return lt[ans]
+
+def skin_net(img):
+    lt = ['pigmented benign keratosis', 'melanoma', 'vascular lesion', 'actinic keratosis', 'squamous cell carcinoma', 'basal cell carcinoma', 'seborrheic keratosis', 'dermatofibroma', 'nevus']
+    img = cv2.resize(img,(224,224))
+    model = tf.keras.models.load_model("Models/skin_cancer.h5")
+    result = model.predict(np.array([img]))
+    ans = np.argmax(result)
+    return lt[ans]