Update app.py

app.py

@@ -17,85 +17,91 @@ st.set_page_config(page_title="Enterprise document search + chat", layout="wide"
 # Streamlit app header
 st.title("Enterprise document helpdesk")
 
+# Initialize session state
+if 'api_key_entered' not in st.session_state:
+    st.session_state.api_key_entered = False
+
 # Sidebar
 with st.sidebar:
     st.header("Configuration")
     api_key = st.text_input("Enter your OpenAI API Key:", type="password")
     if api_key:
         os.environ["OPENAI_API_KEY"] = api_key
+        st.session_state.api_key_entered = True
 
-    st.header('Document Upload and Processing')
-    uploaded_files = st.file_uploader('Upload your files', accept_multiple_files=True, type=['txt', 'pdf', 'csv', 'ppt', 'doc', 'xls', 'pptx', 'xlsx'])
-
-    def load_file(file):
-        file_extension = os.path.splitext(file.name)[1].lower()
-        with tempfile.NamedTemporaryFile(delete=False, suffix=file_extension) as temp_file:
-            temp_file.write(file.getvalue())
-            temp_file_path = temp_file.name
-    
-        if file_extension == '.txt':
-            loader = TextLoader(temp_file_path)
-        elif file_extension == '.pdf':
-            loader = PyPDFLoader(temp_file_path)
-        elif file_extension == '.csv':
-            loader = CSVLoader(temp_file_path)
-        elif file_extension in ['.ppt', '.pptx']:
-            loader = UnstructuredPowerPointLoader(temp_file_path)
-        elif file_extension in ['.doc', '.docx']:
-            loader = UnstructuredWordDocumentLoader(temp_file_path)
-        elif file_extension in ['.xls', '.xlsx']:
-            loader = UnstructuredExcelLoader(temp_file_path)
-        else:
-            os.unlink(temp_file_path)
-            raise ValueError(f"Unsupported file type: {file_extension}")
-    
-        documents = loader.load()
-        os.unlink(temp_file_path)
-        return documents
+    if st.session_state.api_key_entered:
+        st.header('Document Upload and Processing')
+        uploaded_files = st.file_uploader('Upload your files', accept_multiple_files=True, type=['txt', 'pdf', 'csv', 'ppt', 'doc', 'xls', 'pptx', 'xlsx'])
 
-    def summarize_documents(documents):
-        chat = ChatOpenAI(model="gpt-3.5-turbo-1106", temperature=0.2)
+        def load_file(file):
+            file_extension = os.path.splitext(file.name)[1].lower()
+            with tempfile.NamedTemporaryFile(delete=False, suffix=file_extension) as temp_file:
+                temp_file.write(file.getvalue())
+                temp_file_path = temp_file.name
         
-        combined_text = " ".join([doc.page_content for doc in documents])
+            if file_extension == '.txt':
+                loader = TextLoader(temp_file_path)
+            elif file_extension == '.pdf':
+                loader = PyPDFLoader(temp_file_path)
+            elif file_extension == '.csv':
+                loader = CSVLoader(temp_file_path)
+            elif file_extension in ['.ppt', '.pptx']:
+                loader = UnstructuredPowerPointLoader(temp_file_path)
+            elif file_extension in ['.doc', '.docx']:
+                loader = UnstructuredWordDocumentLoader(temp_file_path)
+            elif file_extension in ['.xls', '.xlsx']:
+                loader = UnstructuredExcelLoader(temp_file_path)
+            else:
+                os.unlink(temp_file_path)
+                raise ValueError(f"Unsupported file type: {file_extension}")
         
-        prompt = f"""Summarize the following document in a concise manner, highlighting the key points:
+            documents = loader.load()
+            os.unlink(temp_file_path)
+            return documents
 
-        {combined_text}
+        def summarize_documents(documents):
+            chat = ChatOpenAI(model="gpt-3.5-turbo-1106", temperature=0.2)
+            
+            combined_text = " ".join([doc.page_content for doc in documents])
+            
+            prompt = f"""Summarize the following document in a concise manner, highlighting the key points:
 
-        Summary:"""
-        
-        response = chat.invoke(prompt)
-        return response.content
-
-    # Process uploaded files
-    if uploaded_files:
-        if st.button("Process Documents"):
-            with st.spinner("Processing documents..."):
-                all_documents = []
-                for file in uploaded_files:
-                    all_documents.extend(load_file(file))
-
-                text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-                all_splits = text_splitter.split_documents(all_documents)
-
-                # Store processed documents in session state
-                st.session_state.processed_documents = all_splits
-            st.success("Documents processed successfully!")
+            {combined_text}
+
+            Summary:"""
             
-            # Add a button for summarization
-            if st.button("Generate Summary"):
-                with st.spinner("Generating summary..."):
-                    summary = summarize_documents(st.session_state.processed_documents)
-                    st.session_state.document_summary = summary
-                st.success("Summary generated successfully!")
-
-    # Display the summary if it exists
-    if 'document_summary' in st.session_state:
-        st.subheader("Document Summary")
-        st.write(st.session_state.document_summary)
+            response = chat.invoke(prompt)
+            return response.content
+
+        # Process uploaded files
+        if uploaded_files:
+            if st.button("Process Documents"):
+                with st.spinner("Processing documents..."):
+                    all_documents = []
+                    for file in uploaded_files:
+                        all_documents.extend(load_file(file))
+
+                    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+                    all_splits = text_splitter.split_documents(all_documents)
+
+                    # Store processed documents in session state
+                    st.session_state.processed_documents = all_splits
+                st.success("Documents processed successfully!")
+                
+                # Add a button for summarization
+                if st.button("Generate Summary"):
+                    with st.spinner("Generating summary..."):
+                        summary = summarize_documents(st.session_state.processed_documents)
+                        st.session_state.document_summary = summary
+                    st.success("Summary generated successfully!")
+
+        # Display the summary if it exists
+        if 'document_summary' in st.session_state:
+            st.subheader("Document Summary")
+            st.write(st.session_state.document_summary)
 
 # Main app logic
-if "OPENAI_API_KEY" in os.environ:
+if st.session_state.api_key_entered:
     # Initialize components
     @st.cache_resource
     def initialize_components():
@@ -208,10 +214,10 @@ if "OPENAI_API_KEY" in os.environ:
             st.session_state.memory.save_context({"input": prompt}, {"output": full_response})
 
     else:
-        st.warning("Please upload and process documents to start chatting.")
+        st.info("Please upload and process documents to start chatting.")
 
 else:
-    st.warning("Please enter your OpenAI API Key in the sidebar to start the chatbot.")
+    st.info("Please enter your OpenAI API Key in the sidebar to start.")
 
 # Add a footer
 st.markdown("---")