updated script for top result classifications

app.py

@@ -7,41 +7,75 @@ classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnl
 # Define Streamlit app
 def main():
     # Set page title and favicon
-    st.set_page_config(page_title="Zero-Shot Text Classification", page_icon=":rocket:")
-
-    # App title and description
-    st.title("Zero-Shot Text Classification")
-    st.markdown("""
-    This app performs zero-shot text classification using the Facebook BART-Large-MNLI model.
-    Enter a sentence and candidate labels, and the model will predict the most relevant label.
-    """)
-
-    # Input text box for the sentence to classify
-    sequence_to_classify = st.text_input("Enter the sentence to classify:")
-
-    # Candidate labels input with help text
-    st.text("Enter candidate labels separated by commas (e.g., travel, cooking, dancing):")
-    candidate_labels = st.text_input("Candidate Labels:")
-
-    # Confidence threshold slider
-    confidence_threshold = st.slider("Confidence Threshold:", min_value=0.0, max_value=1.0, value=0.5, step=0.01)
-
-    # Classification button
-    if st.button("Classify"):
-        if sequence_to_classify and candidate_labels:
-            # Split candidate labels into a list
-            candidate_labels = [label.strip() for label in candidate_labels.split(",")]
-
-            # Perform classification
-            classification_result = classifier(sequence_to_classify, candidate_labels)
-
-            # Display classification results
-            st.subheader("Classification Results:")
-            for label, score in zip(classification_result["labels"], classification_result["scores"]):
-                if score >= confidence_threshold:
-                    st.write(f"- {label}: {score}")
+    st.set_page_config(
+        page_title="Zero-Shot Text Classification",
+        page_icon=":rocket:",
+        layout="wide",  # Set layout to wide for better spacing
+        initial_sidebar_state="expanded"  # Expand sidebar by default
+    )
+
+    # App title and description with colorful text
+    st.title(":paintbrush: Zero-Shot Text Classification")
+    st.markdown(
+        """
+        This app performs zero-shot text classification using the Facebook BART-Large-MNLI model.
+        Enter a sentence and candidate labels, and the model will predict the most relevant label.
+        """
+    )
+
+    # Create a two-column layout
+    col1, col2 = st.columns([1, 2])
+
+    # Left pane: Input elements
+    with col1:
+        # Input text box for the sentence to classify
+        sequence_to_classify = st.text_input("Enter the sentence to classify:")
+
+        # Candidate labels input with help text
+        st.text("Enter candidate labels separated by commas (e.g., travel, cooking, dancing):")
+        candidate_labels = st.text_input("Candidate Labels:")
+
+        # Confidence threshold slider with colorful track
+        confidence_threshold = st.slider(
+            "Confidence Threshold:",
+            min_value=0.0,
+            max_value=1.0,
+            value=0.5,
+            step=0.01,
+            key="confidence_threshold",
+            help="Move the slider to adjust the confidence threshold."
+        )
+
+        # Classification button with colorful background
+        classify_button = st.button(
+            "Classify",
+            key="classify_button",
+            help="Click the button to classify the input text with the provided labels."
+        )
+
+    # Right pane: Results
+    with col2:
+        if classify_button:
+            if sequence_to_classify and candidate_labels:
+                # Split candidate labels into a list
+                candidate_labels = [label.strip() for label in candidate_labels.split(",")]
+
+                # Perform classification
+                classification_result = classifier(sequence_to_classify, candidate_labels)
+
+                # Find label with highest score
+                max_score_index = classification_result["scores"].index(max(classification_result["scores"]))
+                max_label = classification_result["labels"][max_score_index]
+                max_score = classification_result["scores"][max_score_index]
+
+                # Display only the label with the highest score
+                if max_score >= confidence_threshold:
+                    st.subheader("Classification Result:")
+                    st.write(f"- **{max_label}**: {max_score:.2f}", unsafe_allow_html=True)
                 else:
-                    st.write(f"- {label}: Below threshold ({score})")
+                    st.subheader("Classification Result:")
+                    st.write(f"- <span style='color: #888;'>{max_label}:</span> Below threshold ({max_score:.2f})", unsafe_allow_html=True)
 
 if __name__ == "__main__":
     main()
+