Update app.py

app.py

@@ -6,6 +6,7 @@ import seaborn as sns
 import plotly.express as px
 import plotly.io as pio
 import plotly.graph_objects as go
+from run import run_pipeline
 
 # Set page configuration
 st.set_page_config(layout="wide")
@@ -36,7 +37,7 @@ df = load_and_clean_data()
 
 
 # Page navigation setup
-page_names = [" GESI Overview", "Sentiment Analysis", "Discrimination Analysis", "Channel Analysis"]
+page_names = ["Analytics Dashboard for Domain Predictions", "GESI Overview", "Sentiment Analysis", "Discrimination Analysis", "Channel Analysis"]
 page = st.sidebar.selectbox("Choose a page", page_names)
 
 # Sidebar Filters
@@ -60,6 +61,64 @@ df_filtered = df[(df['Domain'].isin(domain_filter)) &
 # Define a color palette for consistent visualization styles
 color_palette = px.colors.sequential.Viridis
 
+# Function to render the model prediction visualization page
+def render_prediction_page():
+    st.title("Streamlit Analytics Dashboard for Model Predictions")
+    st.write("""
+    Welcome to the interactive analytics dashboard that brings to life the nuanced assessment of textual content. 
+    Dive into the insightful world of language processing where each sentence you enter is meticulously evaluated 
+    for its domain relevance and sentiment connotation.
+    Instant Analysis: Enter any text snippet and get immediate predictions with our sophisticated model that assesses content with nuanced precision.
+    Domain Identification: Discover the domain categorization of your text, providing clarity on the subject matter with a quantifiable domain score.
+    """)
+
+    # User input text area
+    user_input = st.text_area("Enter Text/Content here to analyze", height=150)
+    
+    if st.button("Perform Contextual Analysis"):
+        # Use run_pipeline to get predictions
+        prediction = run_pipeline(user_input)
+        
+        # Extract prediction details
+        domain_label = prediction.get("domain_label", "Unknown")
+        domain_score = prediction.get("domain_score", 0)
+        discrimination_label = prediction.get("discrimination_label", "Unknown")
+        discrimination_score = prediction.get("discrimination_score", 0)
+        
+        # Visualization layout
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.markdown("#### Domain Label")
+            st.markdown(f"## {domain_label}")
+            st.progress(domain_score)
+        
+        with col2:
+            st.markdown("#### Discrimination Label")
+            st.markdown(f"## {discrimination_label}")
+            st.progress(discrimination_score)
+        
+        col3, col4 = st.columns(2)
+        
+        with col3:
+            # Domain Score Gauge
+            fig_domain = go.Figure(go.Indicator(
+                mode="gauge+number",
+                value=domain_score,
+                domain={'x': [0, 1], 'y': [0, 1]},
+                title={'text': "Domain Score"},
+                gauge={'axis': {'range': [None, 1]}}))
+            st.plotly_chart(fig_domain, use_container_width=True)
+        
+        with col4:
+            # Discrimination Score Gauge
+            fig_discrimination = go.Figure(go.Indicator(
+                mode="gauge+number",
+                value=discrimination_score,
+                domain={'x': [0, 1], 'y': [0, 1]},
+                title={'text': "Discrimination Score"},
+                gauge={'axis': {'range': [None, 1]}}))
+            st.plotly_chart(fig_discrimination, use_container_width=True)
 
 # Visualisation for Domain Distribution
 def create_pie_chart(df, column, title):
@@ -191,7 +250,9 @@ def create_channel_discrimination_chart(df):
 
 # Function for rendering dashboard
 def render_dashboard(page, df_filtered):
-    if page == " GESI Overview":
+    if page == "Analytics Dashboard for Domain Predictions":
+        render_prediction_page()
+    elif page == "GESI Overview":
         st.title(" GESI Overview Dashboard")
         col1, col2 = st.columns(2)
         with col1: