updated streamlit and requirements

streamlit.py

@@ -21,15 +21,15 @@ if image_file:
     st.image(image_file, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
 
 
-model = tf.keras.models.load_model("cnnBoneFracRec.h5")
-target_names = ['Non-Fractured', 'Fractured']
-temp_img = image.load_img(image_file, target_size=(100, 100))
-x = image.img_to_array(temp_img)
-x = np.expand_dims(x, axis=0)
-images = np.vstack([x])
-prediction = np.argmax(model.predict(images), axis=1)
-
-prediction_str = target_names[prediction.item()]
-7
-if prediction_str:
-    st.markdown(f"##### Prediction : {prediction_str}")
+    model = tf.keras.models.load_model("cnnBoneFracRec.h5")
+    target_names = ['Non-Fractured', 'Fractured']
+    temp_img = image.load_img(image_file, target_size=(100, 100))
+    x = image.img_to_array(temp_img)
+    x = np.expand_dims(x, axis=0)
+    images = np.vstack([x])
+    prediction = np.argmax(model.predict(images), axis=1)
+
+    prediction_str = target_names[prediction.item()]
+
+    if prediction_str:
+        st.markdown(f"##### Prediction : {prediction_str}")