Update app.py

app.py

@@ -8,18 +8,14 @@ from langchain.prompts import ChatPromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 import tempfile
 
-# YOUR API KEY
 os.environ["OPENAI_API_KEY"] = "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"
 
 @st.cache_resource
 def load_vectorstore(_file_path):
-    """Load or create vectorstore from uploaded file"""
     if os.path.exists("faiss_index"):
         embeddings = OpenAIEmbeddings()
-        vectorstore = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
-        return vectorstore
+        return FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
     
-    # Process uploaded file
     file_ext = _file_path.split('.')[-1].lower()
     if file_ext == 'pdf':
         loader = PyPDFLoader(_file_path)
@@ -33,24 +29,21 @@ def load_vectorstore(_file_path):
     embeddings = OpenAIEmbeddings()
     vectorstore = FAISS.from_documents(splits, embeddings)
     vectorstore.save_local("faiss_index")
-    
-    st.sidebar.success("✅ Vector index created!")
+    st.sidebar.success("✅ Document indexed!")
     return vectorstore
 
 def get_rag_chain(vectorstore):
-    """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}
+    # FIXED: Clean single-line prompt
+    prompt_template = """You are a helpful assistant. Answer using ONLY this context: {context}
 
 Question: {question}
 
-Answer concisely and accurately. If answer not in context, say "I don't have that information.""""
-    )
+Answer concisely. If not in context: "I don't have that information.""""
+    
+    prompt = ChatPromptTemplate.from_template(prompt_template)
     
     chain = (
         {"context": retriever, "question": lambda x: x} 
@@ -60,80 +53,62 @@ Answer concisely and accurately. If answer not in context, say "I don't have tha
     )
     return chain
 
-# === MAIN UI ===
 st.title("🧠 Dynamic RAG Chatbot")
-st.markdown("**Upload ANY document → Ask ANY question → Get accurate answers!**")
+st.markdown("**Upload PDF/TXT → Ask ANY question → Perfect answers!**")
 
-# File upload
 uploaded_file = st.file_uploader("📤 Upload PDF or TXT", type=['pdf', 'txt'])
 
 if uploaded_file is not None:
-    # Save uploaded file
     with tempfile.NamedTemporaryFile(delete=False, suffix=f".{uploaded_file.name.split('.')[-1]}") as tmp_file:
         tmp_file.write(uploaded_file.getvalue())
         file_path = tmp_file.name
     
     st.success(f"✅ Loaded: {uploaded_file.name}")
     
-    # Load vectorstore
-    with st.spinner("🔄 Indexing your document..."):
+    with st.spinner("🔄 Indexing document..."):
         try:
             vectorstore = load_vectorstore(file_path)
             chain = get_rag_chain(vectorstore)
             st.session_state.chain = chain
             st.session_state.ready = True
-            st.session_state.document_name = uploaded_file.name
+            st.session_state.doc_name = uploaded_file.name
         except Exception as e:
-            st.error(f"Error indexing: {str(e)}")
+            st.error(f"Error: {str(e)}")
             st.session_state.ready = False
 
-# Chat interface
 if 'ready' in st.session_state and st.session_state.ready:
-    st.success(f"🚀 Ready! Chatting about: **{st.session_state.document_name}**")
+    st.success(f"🚀 Ready! Document: **{st.session_state.doc_name}**")
     
     if "messages" not in st.session_state:
         st.session_state.messages = []
     
-    # Chat history
     for message in st.session_state.messages:
         with st.chat_message(message["role"]):
             st.markdown(message["content"])
     
-    # Chat input
-    if query := st.chat_input("💬 Ask ANY question about your document..."):
+    if query := st.chat_input("💬 Ask about your document..."):
         st.session_state.messages.append({"role": "user", "content": query})
         with st.chat_message("user"):
             st.markdown(query)
         
         with st.chat_message("assistant"):
-            with st.spinner("🔍 Searching document..."):
+            with st.spinner("🔍 Searching..."):
                 response = st.session_state.chain.invoke(query)
                 st.markdown(response)
         
         st.session_state.messages.append({"role": "assistant", "content": response})
 
-    # Clear chat
     if st.button("🗑️ Clear Chat"):
         st.session_state.messages = []
         st.rerun()
 
 else:
-    st.info("👆 **Upload a PDF or TXT file to start chatting!**")
-    st.markdown("""
-    **Perfect for:**
-    • 📄 Your **resume/CV**
-    • 📚 Research papers  
-    • 📝 Lecture notes
-    • 💼 Company documents
-    • **ANY text content!**
-    """)
+    st.info("👆 **Upload PDF/TXT to start!**")
+    st.markdown("**Test with:** Resume, notes, research papers...")
 
-# Sidebar
 with st.sidebar:
-    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")
+    st.markdown("### ✅ Medium-Level RAG")
+    st.markdown("- Dynamic upload")
+    st.markdown("- Vector search") 
+    st.markdown("- FAISS indexing")
+    st.markdown("- LCEL pipeline")