upload app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+from PIL import Image
+from src.utils import *
+from src.vertex import *
+
+st.set_page_config(
+    page_title="Hip-Implant Image Classification",
+    page_icon=":robot:",
+    layout="centered",
+    initial_sidebar_state="expanded",
+    menu_items={
+        'How to use': "# Upload an image of a hip-implant (search <loose hip implant> on google), the app will classify the hip-implant as loose or in-control."
+    }
+)
+
+#creating session states
+create_session_state()
+
+
+
+image = Image.open('./image/title.jpg')
+st.image(image)
+st.title(":red[My AI Journey] :blue[Nishant Guvvada] X-ray Assistant")
+
+with st.sidebar:
+    image = Image.open('./image/sidebar_image.jpg')
+    st.image(image)
+    st.markdown("<h2 style='text-align: center; color: red;'>Settings Tab</h2>", unsafe_allow_html=True)
+
+
+    st.write("Model Settings:")
+
+    #define the temeperature for the model
+    temperature_value = st.slider('Temperature :', 0.0, 1.0, 0.2)
+    st.session_state['temperature'] = temperature_value
+
+    #define the temeperature for the model
+    token_limit_value = st.slider('Token limit :', 1, 1024, 256)
+    st.session_state['token_limit'] = token_limit_value
+
+    #define the temeperature for the model
+    top_k_value = st.slider('Top-K  :', 1,40,40)
+    st.session_state['top_k'] = top_k_value
+
+    #define the temeperature for the model
+    top_p_value = st.slider('Top-P :', 0.0, 1.0, 0.8)
+    st.session_state['top_p'] = top_p_value
+
+    if st.button("Reset Session"):
+        reset_session()
+
+
+st.image(bytes_data, caption='User uploaded image')
+st.balloons()
+
+
+@st.cache(allow_output_mutation=True)
+def load_model():
+    model=tf.keras.models.load_model('/Hip-Implant/hip_impant_model.h5')
+    return model
+with st.spinner('Model is being loaded..'):
+    model=load_model()
+
+st.write("""
+         # Image Classification
+         """
+         )
+
+file = st.file_uploader("Upload an X-ray image")
+import cv2
+from PIL import Image, ImageOps
+import numpy as np
+st.set_option('deprecation.showfileUploaderEncoding', False)
+def model_prediction(img, model):
+    resize = tf.image.resize(img, (256,256))
+    yhat = model.predict(np.expand_dims(resize/255, 0))
+    if(yhat>0.5):
+        result = "Prediction is loose"
+    else:
+        result = "Prediction is control"
+    return result
+        
+if file is None:
+    st.text("Please upload an image file")
+else:
+    image = Image.open(file)
+    st.image(image, use_column_width=True)
+    predictions = mode_prediction(image, model)
+    st.write(prediction)
+    print(
+    "This image most likely belongs to {}."
+    .format(prediction)
+)