Update app.py

app.py

@@ -13,10 +13,10 @@ warnings.filterwarnings("ignore")
 os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
 
 st.set_page_config(
-    page_title="PNEUMONIA Disease Detection",
-    page_icon=":skull:",
-    initial_sidebar_state="auto",
-)
+            page_title="ChestAI - Pneumonia Detection",
+            page_icon="🫁",
+            initial_sidebar_state="expanded"
+        )
 
 hide_streamlit_style = """
 	<style>
@@ -34,11 +34,19 @@ def prediction_cls(prediction):
 
 
 with st.sidebar:
-    # st.image("mg.png")
-    st.title("Disease Detection")
-    st.markdown(
-        "Accurate detection of diseases present in the X-Ray. This helps an user to easily detect the disease and identify it's cause."
-    )
+    st.title("👋 Welcome to ChestAI")
+            st.markdown("""
+                ### About
+                ChestAI uses advanced deep learning to detect pneumonia in chest X-rays.
+                
+                ### How to use
+                1. Upload a chest X-ray image (JPG/PNG)
+                2. Wait for the analysis
+                3. View the results and confidence score
+                
+                ### Note
+                This tool is for educational purposes only. Always consult healthcare professionals for medical advice.
+            """)
 st.set_option("deprecation.showfileUploaderEncoding", False)
 
 
@@ -72,9 +80,6 @@ else:
     image = keras.preprocessing.image.load_img(file, target_size=(224, 224), color_mode='rgb')
     st.image(image, caption="Uploaded Image.", use_column_width=True)
     predictions = import_and_predict(image, model)
-    np.random.seed(42)
-    x = random.randint(98, 99) + random.randint(0, 99) * 0.01
-    st.error("Accuracy : " + str(x) + " %")
     print(predictions)
     class_names = [
         "Normal",