updated streamlit and requirements

streamlit.py

@@ -7,20 +7,16 @@ import tensorflow as tf
 from keras.preprocessing import image
 #docker build -t streamlit
 # docker compose up
-
+image_file_prev = ""
 model = tf.keras.models.load_model("cnnBoneFracRec.h5")
 st.markdown("## Bone Fracture Recognition with TensorFlow")
 
 
-
-# actu_loc = [actu + f"_{loc}" for loc in locations]
-# fore_loc = [fore + f"_{loc}" for loc in locations]
-
 image_file = st.file_uploader("Upload X-Ray Image", type=['png', 'jpg'])
 
-if image_file:
+if image_file_prev != image_file and image_file:
     st.image(image_file, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
-
+    image_file_prev = image_file
     target_names = ['Non-Fractured', 'Fractured']
     temp_img = image.load_img(image_file, target_size=(100, 100))
     x = image.img_to_array(temp_img)