Update app.py

app.py

@@ -0,0 +1,33 @@
+import streamlit as st
+from transformers import pipeline
+
+# loarding pipeline
+sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+ner_tagger = pipeline("ner", model="dslim/bert-base-NER", grouped_entities=True)
+
+st.set_page_config(page_title="Customer Support Analyzer", layout="centered")
+st.title("📞 AI Customer service Dialogue Analysis")
+
+# Customer type
+user_input = st.text_area("Please enter the question or conversation：", height=150)
+
+if st.button("Analyse"):
+    if user_input.strip() == "":
+        st.warning("Please enter content")
+    else:
+        with st.spinner("Analysing..."):
+            # Emotion
+            sentiment_result = sentiment_analyzer(user_input)[0]
+            st.subheader("📌 Sentiment analysis results")
+            st.write(f"**Emotional type**: {sentiment_result['label']}")
+            st.write(f"**Confidence degree**: {sentiment_result['score']:.2f}")
+
+            # Command
+            ner_results = ner_tagger(user_input)
+            extracted_entities = [ent['word'] for ent in ner_results if ent['score'] > 0.5]
+
+            st.subheader("🔍Problem keyword recognition")
+            if extracted_entities:
+                st.write(", ".join(set(extracted_entities)))
+            else:
+                st.write("The specific problem keywords were not identified")