Update app.py

app.py

@@ -1,3 +1,4 @@
+
 import streamlit as st
 import numpy as np
 from PIL import Image
@@ -33,7 +34,7 @@ st.title("Deep Prediction Hub")
 st.sidebar.header("Options")
 
 # Choose between tasks
-task = st.sidebar.radio("Select Task", ("Sentiment Classification", "Tumor Detection"))
+task = st.sidebar.radio("Select Task", ("Sentiment Classification", "Tumor Detection"), key="task_selection")
 
 if task == "Sentiment Classification":
     st.subheader("Sentiment Classification")
@@ -44,7 +45,7 @@ if task == "Sentiment Classification":
 
     if new_review_text.strip():
         st.subheader("Choose Model for Sentiment Classification")
-        model_option = st.selectbox("Select Model", ("Perceptron", "Backpropagation", "DNN", "RNN", "LSTM"))
+        model_option = st.selectbox("Select Model", ("Perceptron", "Backpropagation", "DNN", "RNN", "LSTM"), key="model_selection")
 
         # Load models dynamically based on the selected option
         if model_option == "Perceptron":
@@ -80,21 +81,3 @@ elif task == "Tumor Detection":
             st.subheader("Tumor Detection Result")
             st.write(f"**{result}**")
 
-            result = sentiment_classification(new_review_text, model)
-            st.subheader("Sentiment Classification Result")
-            st.write(f"**{result}**")
-
-elif task == "Tumor Detection":
-    st.subheader("Tumor Detection")
-    uploaded_file = st.file_uploader("Choose a tumor image...", type=["jpg", "jpeg", "png"])
-
-    if uploaded_file is not None:
-        # Load the tumor detection model
-        model = load_model('CN.h5')
-        st.image(uploaded_file, caption="Uploaded Image.", use_column_width=False, width=200)
-        st.write("")
-
-        if st.button("Detect Tumor"):
-            result = tumor_detection(uploaded_file, model)
-            st.subheader("Tumor Detection Result")
-            st.write(f"**{result}**")