homework is done

app.py

@@ -1,3 +1,34 @@
 import streamlit as st
+from transformers import BertForSequenceClassification, BertTokenizer, TextClassificationPipeline
 
-st.write("Hello, World!")
+model_path = "JungleLee/bert-toxic-comment-classification"
+tokenizer = BertTokenizer.from_pretrained(model_path)
+model = BertForSequenceClassification.from_pretrained(model_path, num_labels=2)
+
+pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=False)
+
+st.set_page_config(page_title="Message Vibe Checker", page_icon="😬")
+st.title("🕵️‍♂️ Check Your Message Before Sending!")
+st.subheader("Don't let your message get you canceled 😅")
+
+user_message = st.text_area("Paste your message here:", placeholder="e.g., You're a freaking genius!")
+
+if st.button("Analyze Message"):
+    if not user_message.strip():
+        st.warning("You forgot to type something, buddy!")
+    else:
+        with st.spinner("Scanning your message for rage and sass..."):
+            result = pipeline(user_message)[0]
+            label = result['label']
+            score = result['score']
+
+        if label == "TOXIC":
+            st.error(f"☢️ Whoa there! That message might be **toxic** (confidence: {score:.2f})")
+            st.caption("Try sending good vibes instead 🧘‍♂️🌈")
+        else:
+            st.success(f"✅ Looks good! This message seems **clean** (confidence: {score:.2f})")
+            st.balloons()
+            st.caption("Spread kindness like confetti 🎉")
+
+st.markdown("---")
+st.caption("Made with love.")

requirements.txt

@@ -1 +1,2 @@
-transformers[torch] (>=4.51.3,<5.0.0)
+transformers[torch] (>=4.51.3,<5.0.0)
+streamlit==1.33.0