Update app.py

app.py

@@ -3,6 +3,7 @@ import streamlit as st
 from langchain_openai import ChatOpenAI
 from langchain_openai.embeddings import OpenAIEmbeddings
 from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders.pdf import PyMuPDFLoader
 import markdown
 from operator import itemgetter
 from langchain.schema.runnable import RunnablePassthrough
@@ -16,7 +17,7 @@ import os
 import pandas as pd
 import numpy as np
 import datetime
-
+import tempfile
 
 # App config
 load_dotenv()
@@ -33,7 +34,7 @@ st.set_page_config(
     page_icon="🔍",
 )
 
-
+st.session_state.uploaded_files_data = {}
 # Load environment variables
 uploaded_file = None
 topic='employment'
@@ -114,10 +115,26 @@ with sidebar:
     date=str(date)
     prompt = st.button("Summarize News", key="chat_button", use_container_width=True)
 
-    st.subheader("📊 Survey")
-    uploaded_file = st.file_uploader("📂 Upload Pulse Survey (.txt)", type="txt")
+    st.subheader("📊 Document upload")
+    uploaded_file = st.file_uploader("📂 Upload document (.txt, .pdf)", type=["txt", "pdf"])
     st.session_state['uploaded_file'] = uploaded_file
-    prompt_survey = st.button("Survey results", key="chat_button1", use_container_width=True)
+    if uploaded_file:
+        if uploaded_file.name not in st.session_state.uploaded_files_data:
+            file_suffix = ".pdf" if uploaded_file.name.lower().endswith(".pdf") else ".txt"
+            with tempfile.NamedTemporaryFile(delete=False, suffix=file_suffix) as temp:
+                temp.write(uploaded_file.read())
+                temp_path = temp.name
+                # Store the temp path in session state
+                st.session_state.uploaded_files_data[uploaded_file.name] = {
+                    'path': temp_path,
+                    'type': file_suffix
+                }
+        else:
+            # Use the stored path
+            temp_path = st.session_state.uploaded_files_data[uploaded_file.name]['path']
+            file_suffix = st.session_state.uploaded_files_data[uploaded_file.name]['type']
+    
+    prompt_survey = st.button("Results", key="chat_button1", use_container_width=True)
 
 # Handle search submission
 
@@ -298,7 +315,7 @@ if "openai_model" not in st.session_state:
 prompt1 = st.chat_input("Type your additional questions here...")
 
 # Suggested keywords with enhanced styling
-suggested_keywords = ["Latest News", "News on remote work", f"Survey sentiment", f"Employee satisfaction", f"How many employees are males?"]
+suggested_keywords = ["Latest News", "News on remote work", f"Survey sentiment", f"Employee satisfaction", f"How many employees?"]
 st.markdown("**Suggested Keywords:**")
 cols = st.columns(len(suggested_keywords))
 for idx, keyword in enumerate(suggested_keywords):
@@ -307,12 +324,26 @@ for idx, keyword in enumerate(suggested_keywords):
 
 if prompt1:
     st.session_state.messages.append({"role": "user", "content": prompt1})
-    with open('./data/employee_pulse_survey.txt', 'r') as file:
-        survey_txt = file.read()
+    try:
+        temp_path = st.session_state.uploaded_files_data[uploaded_file.name]['path']
+        file_suffix = st.session_state.uploaded_files_data[uploaded_file.name]['type']
+        survey_txt=''
+        handbook_txt=''
+        
+        if st.session_state.uploaded_files_data[uploaded_file.name]['path']:
+            if file_suffix == ".pdf":
+                docs = PyMuPDFLoader(temp_path).load()
+            handbook_txt = "\n".join(d.page_content for d in docs)
+        else:
+            survey_txt = uploaded_file.read().decode('utf-8')
+    except Exception as e:
+        st.write(f"Upload your survey or handbook document to analyze it.")
+        survey_txt = ''
+        handbook_txt = ''
     df_linkedin=pd.read_csv('./data/linkedin_post_result.csv', usecols=['postTimestamp','postUrl','postContent','author'])
     df_linkedin_selected=df_linkedin.values.flatten()
     docs_text_linkedin = "\n".join([f"- {value}" for value in df_linkedin_selected if not pd.isna(value)])
-    survey_txt_all=survey_txt+'\n'+'LinkedIn posts:'+'\n'+docs_text_linkedin
+    survey_txt_all=survey_txt+'\n'+'LinkedIn posts:'+'\n'+docs_text_linkedin +'\n'+'Employee handbook:'+'\n'+handbook_txt
     with open('./data/events.txt', 'r') as file:
         events_txt = file.read()
     
@@ -327,7 +358,7 @@ if prompt1:
         response = base_llm.invoke(f"""You are the Supervisor of the EVE company. In your team you have, general conversation analyst, data analyst, survey analyst and news article analyst. 
                                         If the question {prompt1} can be answered from the history of the conversation:{st.session_state.messages[-10:]}, from events file: {events_txt} or you can use your knowledge respond 'history' with the exception of the reddit news those leave to 'news' team member.
                                         If not: decide if the question: '{prompt1}' is about employees data (such as about Betsy or Katerina or Steven). You have employees data in a huge database with the following columns: {database_columns}, it has information about all employees. If yes, respond 'data'.
-                                        If not: decide if the question is asking about the survey: {survey_txt}. If yes, respond 'survey'.
+                                        If not: decide if the question is asking about the survey or employee handbook: {survey_txt}. If yes, respond 'survey'.
                                         If not: decide if the question is asking about linkedin posts: {docs_text_linkedin}. If yes, respond 'history'.
                                         If not: decide if the question is asking about news articles on employment trends or remote work or reddit news articles. If yes, respond 'news'.
                                         Your response will be either 'history' or 'data' or 'survey' or 'news'. 
@@ -374,7 +405,8 @@ if prompt1:
             print('survey')
             # SURVEY AGENT
             import survey_agent1
-            response = survey_agent1.analyze_survey_document(survey_txt, f'the question is: {prompt1} and the history is: {st.session_state.messages[-10:]}')
+            print(survey_txt_all)
+            response = survey_agent1.analyze_survey_document(survey_txt_all, f'the question is: {prompt1} and the history is: {st.session_state.messages[-10:]}')
             st.session_state.messages.append({"role": "survey_agent", "content": response})
             # st.chat_message("assistant").write(str(response))