Update app.py

app.py

@@ -7,69 +7,41 @@ 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:",
-        layout="wide",  # Set layout to wide for better spacing
-        initial_sidebar_state="expanded"  # Expand sidebar by default
-    )
+    st.set_page_config(page_title="Zero-Shot Text Classification", page_icon=":rocket:")
 
-    # App title and description with colorful text
+    # 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.
-        """
-    )
-
-    # 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)
-
-                # Display classification results with colorful labels
-                st.subheader("Classification Results:")
-                for label, score in zip(classification_result["labels"], classification_result["scores"]):
-                    if score >= confidence_threshold:
-                        st.write(f"- **{label}**: {score:.2f}", unsafe_allow_html=True)
-                    else:
-                        st.write(f"- <span style='color: #888;'>{label}:</span> Below threshold ({score:.2f})", unsafe_allow_html=True)
+    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}")
+                else:
+                    st.write(f"- {label}: Below threshold ({score})")
 
 if __name__ == "__main__":
     main()