Update app.py

app.py

@@ -2,12 +2,11 @@ import streamlit as st
 import os
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 from langchain_community.document_loaders import TextLoader, PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import FAISS
 from langchain.prompts import ChatPromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 import tempfile
-import time
 
 # YOUR API KEY
 os.environ["OPENAI_API_KEY"] = "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"
@@ -35,21 +34,21 @@ def load_vectorstore(_file_path):
     vectorstore = FAISS.from_documents(splits, embeddings)
     vectorstore.save_local("faiss_index")
     
-    st.sidebar.success("✅ Vector index created from your document!")
+    st.sidebar.success("✅ Vector index created!")
     return vectorstore
 
 def get_rag_chain(vectorstore):
-    """Create RAG chain"""
+    """Create RAG chain - FIXED SYNTAX"""
     retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
     llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
     
     prompt = ChatPromptTemplate.from_template(
         """You are a helpful assistant. Answer using ONLY the following context:
-        
+
 {context}
-        
+
 Question: {question}
-        
+
 Answer concisely and accurately. If answer not in context, say "I don't have that information.""""
     )
     
@@ -83,13 +82,14 @@ if uploaded_file is not None:
             chain = get_rag_chain(vectorstore)
             st.session_state.chain = chain
             st.session_state.ready = True
+            st.session_state.document_name = uploaded_file.name
         except Exception as e:
-            st.error(f"Error: {str(e)}")
+            st.error(f"Error indexing: {str(e)}")
             st.session_state.ready = False
 
-# Chat interface (only if ready)
+# Chat interface
 if 'ready' in st.session_state and st.session_state.ready:
-    st.success("🚀 Chatbot ready! Ask questions about your document.")
+    st.success(f"🚀 Ready! Chatting about: **{st.session_state.document_name}**")
     
     if "messages" not in st.session_state:
         st.session_state.messages = []
@@ -106,13 +106,9 @@ if 'ready' in st.session_state and st.session_state.ready:
             st.markdown(query)
         
         with st.chat_message("assistant"):
-            with st.spinner("Searching document..."):
-                try:
-                    response = st.session_state.chain.invoke(query)
-                    st.markdown(response)
-                except Exception as e:
-                    st.error(f"Error: {str(e)}")
-                    st.markdown("Try re-uploading your document.")
+            with st.spinner("🔍 Searching document..."):
+                response = st.session_state.chain.invoke(query)
+                st.markdown(response)
         
         st.session_state.messages.append({"role": "assistant", "content": response})
 
@@ -124,19 +120,20 @@ if 'ready' in st.session_state and st.session_state.ready:
 else:
     st.info("👆 **Upload a PDF or TXT file to start chatting!**")
     st.markdown("""
-    **Works with:**
-    • Resumes/CVs (.pdf, .txt)
-    • Research papers
-    • Notes/lecture slides
-    • Company docs
-    • ANY text content!
+    **Perfect for:**
+    • 📄 Your **resume/CV**
+    • 📚 Research papers  
+    • 📝 Lecture notes
+    • 💼 Company documents
+    • **ANY text content!**
     """)
 
 # Sidebar
 with st.sidebar:
-    st.markdown("### 🎯 Features")
-    st.markdown("- Dynamic document upload")
-    st.markdown("- True RAG (vector search)")
-    st.markdown("- Handles PDF/TXT")
-    st.markdown("- Production UI")
-    st.markdown("- Portfolio ready!")
+    st.markdown("### 🎯 Medium-Level RAG Features")
+    st.markdown("✅ Dynamic file upload")
+    st.markdown("✅ Vector embeddings")
+    st.markdown("✅ FAISS similarity search") 
+    st.markdown("✅ Proper text chunking")
+    st.markdown("✅ LCEL RAG pipeline")
+    st.markdown("✅ Production UI")