Update backupapp.py

backupapp.py

@@ -18,7 +18,6 @@ from xml.etree import ElementTree as ET
 from bs4 import BeautifulSoup
 from collections import deque
 from audio_recorder_streamlit import audio_recorder
-
 from dotenv import load_dotenv
 from PyPDF2 import PdfReader
 from langchain.text_splitter import CharacterTextSplitter
@@ -29,7 +28,9 @@ from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
 from templates import css, bot_template, user_template
 
-
+# page config and sidebar declares up front allow all other functions to see global class variables
+st.set_page_config(page_title="GPT Streamlit Document Reasoner", layout="wide")
+should_save = st.sidebar.checkbox("💾 Save")
 
 def generate_filename(prompt, file_type):
     central = pytz.timezone('US/Central')
@@ -53,7 +54,10 @@ def transcribe_audio(openai_key, file_path, model):
         #st.write('Responses:')
         #st.write(chatResponse)
         filename = generate_filename(transcript, 'txt')
-        create_file(filename, transcript, chatResponse)
+        #create_file(filename, transcript, chatResponse)
+        response = chatResponse
+        user_prompt = transcript
+        create_file(filename, user_prompt, response, should_save)
         return transcript
     else:
         st.write(response.json())
@@ -70,7 +74,9 @@ def save_and_play_audio(audio_recorder):
         return filename
     return None
 
-def create_file(filename, prompt, response):
+def create_file(filename, prompt, response, should_save=True):
+    if not should_save:
+        return
     if filename.endswith(".txt"):
         with open(filename, 'w') as file:
             file.write(f"{prompt}\n{response}")
@@ -273,8 +279,10 @@ def process_user_input(user_question):
         st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
         # Save file output from PDF query results
         filename = generate_filename(user_question, 'txt')
-        create_file(filename, user_question, message.content)
-        
+        #create_file(filename, user_question, message.content)
+        response = message.content
+        user_prompt = user_question
+        create_file(filename, user_prompt, response, should_save)       
         #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
 
 def divide_prompt(prompt, max_length):
@@ -294,21 +302,19 @@ def divide_prompt(prompt, max_length):
     return chunks
     
 def main():
-    # Sidebar and global
     openai.api_key = os.getenv('OPENAI_API_KEY')
-    st.set_page_config(page_title="GPT Streamlit Document Reasoner",layout="wide")
 
     # File type for output, model choice
-    menu = ["txt", "htm", "xlsx", "csv", "md", "py"]  #619
+    menu = ["txt", "htm", "xlsx", "csv", "md", "py"]
     choice = st.sidebar.selectbox("Output File Type:", menu)
     model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))
-    
+
     # Audio, transcribe, GPT:
     filename = save_and_play_audio(audio_recorder)
     if filename is not None:
         transcription = transcribe_audio(openai.api_key, filename, "whisper-1")
         st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-        filename=None # since transcription is finished next time just use the saved transcript
+        filename = None
 
     # prompt interfaces
     user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)
@@ -318,9 +324,11 @@ def main():
     with collength:
         max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
     with colupload:
-        uploaded_file = st.file_uploader("Add a file for context:", type=["pdf", "xml", "json", "xlsx","csv","html", "htm", "md", "txt"])
+        uploaded_file = st.file_uploader("Add a file for context:", type=["pdf", "xml", "json", "xlsx", "csv", "html", "htm", "md", "txt"])
+
 
     # Document section chat
+        
     document_sections = deque()
     document_responses = {}
     if uploaded_file is not None:
@@ -343,7 +351,7 @@ def main():
                     st.write(response)
                     document_responses[i] = response
                     filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
-                    create_file(filename, user_prompt, response)
+                    create_file(filename, user_prompt, response, should_save)
                     st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
 
     if st.button('💬 Chat'):
@@ -367,14 +375,20 @@ def main():
         st.write(response)
         
         filename = generate_filename(user_prompt, choice)
-        create_file(filename, user_prompt, response)
+        create_file(filename, user_prompt, response, should_save)
         st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
 
     all_files = glob.glob("*.*")
     all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20]  # exclude files with short names
     all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)  # sort by file type and file name in descending order
 
-    # sidebar of files
+    # Sidebar of Files Saving History and surfacing files as context of prompts and responses
+    # Added "Delete All" button
+    if st.sidebar.button("🗑 Delete All"):
+        for file in all_files:
+            os.remove(file)
+        st.experimental_rerun()
+        
     file_contents=''
     next_action=''
     for file in all_files:
@@ -411,7 +425,7 @@ def main():
             st.write('Reasoning with your inputs...')
             response = chat_with_model(user_prompt, file_contents, model_choice)
             filename = generate_filename(file_contents, choice)
-            create_file(filename, file_contents, response)
+            create_file(filename, user_prompt, response, should_save)
 
             st.experimental_rerun()
             #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
@@ -439,5 +453,5 @@ with st.sidebar:
             st.session_state.conversation = get_chain(vectorstore)
             st.markdown('# AI Search Index of Length:' + length + ' Created.')  # add timing
             filename = generate_filename(raw, 'txt')
-            create_file(filename, raw, '')
-            
+            create_file(filename, raw, '', should_save)
+            #create_file(filename, raw, '')