Update app.py run the model

app.py

@@ -1,42 +1,46 @@
 import streamlit as st
+from transformers import pipeline
 
-# Set up the page layout and title
-st.title("Entity and Aspect Analyzer")
+# Initialize the model and tokenizer once, to avoid reloading them on each user interaction
+@st.cache(allow_output_mutation=True)
+def load_model():
+    classifier = pipeline("text-classification", model="tweetpie/toxic-content-classifier")
+    return classifier
 
-# Sidebar for model selection
+# Set up the title
+st.title("Toxic Content Classifier Dashboard")
+
+# Sidebar setup for configuration
+st.sidebar.header("Configuration")
 model_selection = st.sidebar.selectbox(
     "Select a model",
     options=['alm', 'blm'],
     index=0  # Default selection
 )
 
-# Set up two columns for inputs
-col1, col2 = st.columns(2)
+# Sidebar inputs with headers for entities and aspects
+st.sidebar.header("Entities")
+pro_entities = st.sidebar.text_input("Pro Entities", help="Enter pro entities separated by commas")
+anti_entities = st.sidebar.text_input("Anti Entities", help="Enter anti entities separated by commas")
+neutral_entities = st.sidebar.text_input("Neutral Entities", help="Enter neutral entities separated by commas")
 
-with col1:
-    st.header("Entities")
-    # Free-text inputs for entities
-    pro_entities = st.text_input("Pro Entities", help="Enter pro entities separated by commas")
-    anti_entities = st.text_input("Anti Entities", help="Enter anti entities separated by commas")
-    neutral_entities = st.text_input("Neutral Entities", help="Enter neutral entities separated by commas")
+st.sidebar.header("Aspects")
+pro_aspects = st.sidebar.text_input("Pro Aspects", help="Enter pro aspects separated by commas")
+anti_aspects = st.sidebar.text_input("Anti Aspects", help="Enter anti aspects separated by commas")
+neutral_aspects = st.sidebar.text_input("Neutral Aspects", help="Enter neutral aspects separated by commas")
 
-with col2:
-    st.header("Aspects")
-    # Free-text inputs for aspects
-    pro_aspects = st.text_input("Pro Aspects", help="Enter pro aspects separated by commas")
-    anti_aspects = st.text_input("Anti Aspects", help="Enter anti aspects separated by commas")
-    neutral_aspects = st.text_input("Neutral Aspects", help="Enter neutral aspects separated by commas")
+generate_button = st.sidebar.button("Generate")
 
-# Generate button
-generate_button = st.button("Generate")
+# Load the model
+classifier = load_model()
 
+# Process the input text and generate output
 if generate_button:
-    # Here, you can process the inputs or call a model based on the selected 'alm' or 'blm' option
-    # For demonstration, we're just displaying the inputs
-    st.write(f"Model Selected: {model_selection}")
-    st.write(f"Pro Entities: {pro_entities}")
-    st.write(f"Anti Entities: {anti_entities}")
-    st.write(f"Neutral Entities: {neutral_entities}")
-    st.write(f"Pro Aspects: {pro_aspects}")
-    st.write(f"Anti Aspects: {anti_aspects}")
-    st.write(f"Neutral Aspects: {neutral_aspects}")
+    with st.spinner('Processing...'):
+        # Call the model with the input text
+        input_text = "I love you"
+        model_output = classifier(input_text)
+        
+        # Displaying the input and model's output
+        st.write(f"Input Text: {input_text}")
+        st.write("Model Output:", model_output)