added app.py file

app.py

@@ -0,0 +1,47 @@
+import streamlit as st
+from transformers import pipeline
+
+# Load the zero-shot classification model
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+
+# 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}")
+                else:
+                    st.write(f"- {label}: Below threshold ({score})")
+
+if __name__ == "__main__":
+    main()