updated to working version

app.py

@@ -1,94 +1,43 @@
 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()
-
+import cv2
+import numpy as np
+from PIL import Image, ImageOps
+import imageio.v3 as iio
 
-@st.cache(allow_output_mutation=True)
+@st.cache_resource()
 def load_model():
-    model=tf.keras.models.load_model('/Hip-Implant/hip_impant_model.h5')
+    model=tf.keras.models.load_model('./hip_impant_model.h5')
     return model
-with st.spinner('Model is being loaded..'):
-    model=load_model()
-
+    
+st.title(":blue[Nishant Guvvada's] :red[AI Journey]  The Hip-Implant X-ray Assistant")
+image = Image.open('./title.jpg')
+st.image(image)
 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))
+file = st.file_uploader("Upload an X-ray image", type= ['png', 'jpg'])
+
+def model_prediction(path):
+    resize = tf.image.resize(path, (256,256))
+    with st.spinner('Model is being loaded..'):
+        model=load_model()
     yhat = model.predict(np.expand_dims(resize/255, 0))
-    if(yhat>0.5):
-        result = "Prediction is loose"
+    return yhat
+
+def on_click():
+    if file is None:
+        st.text("Please upload an image file")
     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)
-)
+        image = Image.open(file)
+        st.image(image, use_column_width=True)
+        image = image.convert('RGB')
+        predictions = model_prediction(np.array(image))
+        if (predictions>0.5):
+            st.write("""# Prediction : Implant is loose""")
+        else:
+            st.write("""# Prediction : Implant is in control""")
+
+st.button('Predict', on_click=on_click)