Update app.py

app.py

@@ -1,5 +1,5 @@
 # app.py
-# PromptPilot: Unified AI Chatbot with 5 Lightweight Modes (AML-3304 Assignment)
+# PromptPilot: Unified AI Chatbot with 5 Modes (AML-3304 Assignment)
 
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM
@@ -25,8 +25,8 @@ with cols[2]:
     if st.button("📊 Bayesian Q&A"):
         st.session_state.mode = 'Bayesian Q&A'
 with cols[3]:
-    if st.button("🌐 Lightweight Coder"):
-        st.session_state.mode = 'Lightweight Coder'
+    if st.button("🌐 Deep Coder"):
+        st.session_state.mode = 'Deep Coder'
 with cols[4]:
     if st.button("🔁 Transformer Explorer"):
         st.session_state.mode = 'Transformer Explorer'
@@ -76,12 +76,12 @@ if user_input and st.session_state.mode:
             st.subheader("📊 Bayesian-style Answer:")
             st.write(result)
 
-        elif st.session_state.mode == 'Lightweight Coder':
+        elif st.session_state.mode == 'Deep Coder':
             tokenizer, model = load_codegen()
             inputs = tokenizer(user_input, return_tensors="pt")
             outputs = model.generate(inputs["input_ids"], max_new_tokens=128, do_sample=True, temperature=0.8)
             result = tokenizer.decode(outputs[0], skip_special_tokens=True)
-            st.subheader("🌐 Lightweight Code:")
+            st.subheader("🌐 Code Output:")
             st.code(result, language="python")
 
         elif st.session_state.mode == 'Transformer Explorer':
@@ -90,5 +90,5 @@ if user_input and st.session_state.mode:
             inputs = tokenizer(prompt, return_tensors="pt")
             outputs = model.generate(**inputs, max_new_tokens=100)
             result = tokenizer.decode(outputs[0], skip_special_tokens=True)
-            st.subheader("🔁 Paraphrased by Transformer:")
+            st.subheader("🔁 Paraphrased Text:")
             st.write(result)