Update app.py

app.py

@@ -9,111 +9,93 @@ from langchain.memory import ConversationBufferMemory
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.document_loaders import PyMuPDFLoader
 
-# App config
-st.set_page_config(page_title="Café Eleven", page_icon="☕")
-st.title("☕ Café Eleven Ordering Assistant")
+def main():
+    # App config
+    st.set_page_config(page_title="Café Eleven", page_icon="☕")
+    st.title("☕ Café Eleven Ordering Assistant")
 
-# Initialize chat
-if "messages" not in st.session_state:
-    st.session_state.messages = [{
-        "role": "assistant", 
-        "content": "Hi! Welcome to Café Eleven. What would you like to order today?"
-    }]
+    # Initialize chat (must be inside main())
+    if "messages" not in st.session_state:
+        st.session_state.messages = [{
+            "role": "assistant", 
+            "content": "Hi! Welcome to Café Eleven. What would you like to order today?"
+        }]
 
-# Display messages
-for msg in st.session_state.messages:
-    st.chat_message(msg["role"]).write(msg["content"])
+    # Display messages
+    for msg in st.session_state.messages:
+        st.chat_message(msg["role"]).write(msg["content"])
 
-# Chat functions
-@st.cache_resource
-def load_chain():
-    # Load all PDFs in directory
-    pdf_files = [str(p) for p in Path(".").glob("*.pdf")]
-    if not pdf_files:
-        st.error("No PDF files found! Please upload menu PDFs.")
-        st.stop()
-    
-    # Load and split documents
-    docs = []
-    for pdf in pdf_files:
-        loader = PyMuPDFLoader(pdf)
-        docs.extend(loader.load())
-    
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=1000,
-        chunk_overlap=200
-    )
-    splits = text_splitter.split_documents(docs)
-    
-    # Setup vectorstore
-    embeddings = HuggingFaceEmbeddings(
-        model_name="sentence-transformers/all-mpnet-base-v2"
-    )
-    vectorstore = FAISS.from_documents(splits, embeddings)
-    
-    # Setup LLM
-    llm = HuggingFaceHub(
-        repo_id="meta-llama/Llama-2-7b-chat-hf",
-        huggingfacehub_api_token=os.environ["HF_TOKEN"],
-        model_kwargs={
-            "temperature": 0.2,
-            "max_length": 256
-        }
-    )
-    
-    # Create chain with system prompt
-    return ConversationalRetrievalChain.from_llm(
-        llm=llm,
-        retriever=vectorstore.as_retriever(),
-        memory=ConversationBufferMemory(
-            memory_key="chat_history",
-            return_messages=True
-        ),
-        condense_question_prompt="""
-        You are a friendly café assistant. Your job is to:
-        1. Greet customers warmly
-        2. Help them place orders
-        3. Suggest menu items
-        4. Never make up items not in the menu
-        Current conversation:
-        {chat_history}
-        Question: {question}
-        """
-    )
+    # Chat functions
+    @st.cache_resource
+    def load_chain():
+        pdf_files = [str(p) for p in Path(".").glob("*.pdf")]
+        if not pdf_files:
+            st.error("No PDF files found! Please upload menu PDFs.")
+            st.stop()
+        
+        docs = []
+        for pdf in pdf_files:
+            loader = PyMuPDFLoader(pdf)
+            docs.extend(loader.load())
+        
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1000,
+            chunk_overlap=200
+        )
+        splits = text_splitter.split_documents(docs)
+        
+        embeddings = HuggingFaceEmbeddings(
+            model_name="sentence-transformers/all-mpnet-base-v2"
+        )
+        vectorstore = FAISS.from_documents(splits, embeddings)
+        
+        llm = HuggingFaceHub(
+            repo_id="meta-llama/Llama-2-7b-chat-hf",
+            huggingfacehub_api_token=os.environ["HF_TOKEN"],
+            model_kwargs={
+                "temperature": 0.2,
+                "max_length": 256
+            }
+        )
+        
+        return ConversationalRetrievalChain.from_llm(
+            llm=llm,
+            retriever=vectorstore.as_retriever(),
+            memory=ConversationBufferMemory(
+                memory_key="chat_history",
+                return_messages=True
+            )
+        )
 
-# Chat input
-if prompt := st.chat_input("Your order..."):
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    st.chat_message("user").write(prompt)
-    
-    with st.spinner("Preparing your order..."):
-        try:
-            chain = load_chain()
-            response = chain({"question": prompt})["answer"]
-            
-            # Format response cleanly
-            if "menu" in prompt.lower():
-                response = "Here are our offerings:\n" + response
-            elif "thank" in prompt.lower():
-                response = "You're welcome! " + response
-            
-            st.session_state.messages.append({"role": "assistant", "content": response})
-            st.chat_message("assistant").write(response)
-            
-        except Exception as e:
-            st.error(f"Sorry, something went wrong: {str(e)}")
+    # Chat input
+    if prompt := st.chat_input("Your order..."):
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        st.chat_message("user").write(prompt)
+        
+        with st.spinner("Preparing your order..."):
+            try:
+                chain = load_chain()
+                response = chain({"question": prompt})["answer"]
+                st.session_state.messages.append({"role": "assistant", "content": response})
+                st.chat_message("assistant").write(response)
+            except Exception as e:
+                st.error(f"Sorry, something went wrong: {str(e)}")
 
-# PDF upload section
-with st.sidebar:
-    st.subheader("Menu Management")
-    uploaded_files = st.file_uploader(
-        "Upload PDF menus",
-        type="pdf",
-        accept_multiple_files=True
-    )
-    if uploaded_files:
-        for file in uploaded_files:
-            with open(file.name, "wb") as f:
-                f.write(file.getbuffer())
-        st.success(f"Uploaded {len(uploaded_files)} menu(s)")
-        st.cache_resource.clear()  # Refresh the vectorstore
+    # PDF upload
+    with st.sidebar:
+        st.subheader("Menu Management")
+        uploaded_files = st.file_uploader(
+            "Upload PDF menus",
+            type="pdf",
+            accept_multiple_files=True
+        )
+        if uploaded_files:
+            for file in uploaded_files:
+                with open(file.name, "wb") as f:
+                    f.write(file.getbuffer())
+            st.success(f"Uploaded {len(uploaded_files)} menu(s)")
+            st.cache_resource.clear()
+
+# Required for Hugging Face Spaces
+if __name__ == "__main__":
+    main()