Create app.py

app.py

@@ -0,0 +1,29 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+
+# Load the model and tokenizer
+model_name = "AdilHayat173/token_classifcation"  
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
+
+st.title("Token Classification with Hugging Face")
+
+# Text input from user
+user_input = st.text_area("Enter text for token classification:", "")
+
+if st.button("Classify Text"):
+    if user_input:
+        # Token classification
+        results = nlp(user_input)
+        
+        # Display results
+        st.write("### Token Classification Results")
+        for entity in results:
+            st.write(f"**Token:** {entity['word']}")
+            st.write(f"**Label:** {entity['entity_group']}")
+            st.write(f"**Score:** {entity['score']:.4f}")
+            st.write("---")
+    else:
+        st.write("Please enter some text for classification.")