# # # import streamlit as st # # # from dotenv import load_dotenv # # # from PyPDF2 import PdfReader # # # from langchain.text_splitter import CharacterTextSplitter # # # from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings # # # from langchain.vectorstores import FAISS # # # from langchain.chat_models import ChatOpenAI # # # from langchain.memory import ConversationBufferMemory # # # from langchain.chains import ConversationalRetrievalChain # # # from htmlTemplates import css, bot_template, user_template # # # from langchain.llms import HuggingFaceHub # # # def get_pdf_text(pdf_docs): # # # text = "" # # # for pdf in pdf_docs: # # # pdf_reader = PdfReader(pdf) # # # for page in pdf_reader.pages: # # # text += page.extract_text() # # # return text # # # def get_text_chunks(text): # # # text_splitter = CharacterTextSplitter( # # # separator="\n", # # # chunk_size=1000, # # # chunk_overlap=200, # # # length_function=len # # # ) # # # chunks = text_splitter.split_text(text) # # # return chunks # # # def get_vectorstore(text_chunks): # # # embeddings = OpenAIEmbeddings() # # # # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") # # # vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) # # # return vectorstore # # # def get_conversation_chain(vectorstore): # # # llm = ChatOpenAI() # # # # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512}) # # # memory = ConversationBufferMemory( # # # memory_key='chat_history', return_messages=True) # # # conversation_chain = ConversationalRetrievalChain.from_llm( # # # llm=llm, # # # retriever=vectorstore.as_retriever(), # # # memory=memory # # # ) # # # return conversation_chain # # # def handle_userinput(user_question): # # # response = st.session_state.conversation({'question': user_question}) # # # st.session_state.chat_history = response['chat_history'] # # # for i, message in enumerate(st.session_state.chat_history): # # # if i % 2 == 0: # # # st.write(user_template.replace( # # # "{{MSG}}", message.content), unsafe_allow_html=True) # # # else: # # # st.write(bot_template.replace( # # # "{{MSG}}", message.content), unsafe_allow_html=True) # # # def main(): # # # load_dotenv() # # # st.set_page_config(page_title="Mental Health Support", # # # page_icon=":books:") # # # st.write(css, unsafe_allow_html=True) # # # if "conversation" not in st.session_state: # # # st.session_state.conversation = None # # # if "chat_history" not in st.session_state: # # # st.session_state.chat_history = None # # # st.header("Mental Health Support :brain:") # # # user_question = st.text_input("Ask a question about your documents:") # # # if user_question: # # # handle_userinput(user_question) # # # with st.sidebar: # # # st.subheader("Your documents") # # # pdf_docs = st.file_uploader( # # # "Upload your PDFs here and click on 'Process'", accept_multiple_files=True) # # # if st.button("Process"): # # # with st.spinner("Processing"): # # # # get pdf text # # # raw_text = get_pdf_text(pdf_docs) # # # # get the text chunks # # # text_chunks = get_text_chunks(raw_text) # # # # create vector store # # # vectorstore = get_vectorstore(text_chunks) # # # # create conversation chain # # # st.session_state.conversation = get_conversation_chain( # # # vectorstore) # # # if __name__ == '__main__': # # # main() # # # import streamlit as st # # # from dotenv import load_dotenv # # # from PyPDF2 import PdfReader # # # from langchain.text_splitter import CharacterTextSplitter # # # from langchain.embeddings import OpenAIEmbeddings # # # # from langchain.embeddings import HuggingFaceInstructEmbeddings # # # from langchain.vectorstores import FAISS # # # from langchain.chat_models import ChatOpenAI # # # from langchain.memory import ConversationBufferMemory # # # from langchain.chains import ConversationalRetrievalChain # # # from htmlTemplates import css, bot_template, user_template # # # # from langchain.llms import HuggingFaceHub # # # # from streamlit_option_menu import option_menu # # # import pyttsx3 # # # def get_pdf_text(pdf_paths): # # # text = "" # # # for pdf_path in pdf_paths: # # # with open(pdf_path, 'rb') as pdf_file: # # # pdf_reader = PdfReader(pdf_file) # # # for page in pdf_reader.pages: # # # text += page.extract_text() # # # return text # # # def get_text_chunks(text): # # # text_splitter = CharacterTextSplitter( # # # separator="\n", # # # chunk_size=1000, # # # chunk_overlap=200, # # # length_function=len # # # ) # # # chunks = text_splitter.split_text(text) # # # return chunks # # # def get_vectorstore(text_chunks): # # # embeddings = OpenAIEmbeddings() # # # #embeddings = HuggingFaceInstructEmbeddings(model_name="nomic-ai/gpt4all-j") # # # vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) # # # return vectorstore # # # def get_conversation_chain(vectorstore): # # # llm = ChatOpenAI() # # # #llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512}) # # # memory = ConversationBufferMemory( # # # memory_key='chat_history', return_messages=True) # # # conversation_chain = ConversationalRetrievalChain.from_llm( # # # llm=llm, # # # retriever=vectorstore.as_retriever(), # # # memory=memory # # # ) # # # return conversation_chain # # # def handle_userinput(user_question): # # # response = st.session_state.conversation({'question': user_question}) # # # st.session_state.chat_history = response['chat_history'] # # # for i, message in enumerate(st.session_state.chat_history): # # # if i % 2 == 0: # # # st.write(user_template.replace( # # # "{{MSG}}", message.content), unsafe_allow_html=True) # # # else: # # # st.write(bot_template.replace( # # # "{{MSG}}", message.content), unsafe_allow_html=True) # # # engine = pyttsx3.init() # # # engine.say(response['answer']) # # # engine.runAndWait() # # # def main(): # # # load_dotenv() # # # st.set_page_config(page_title="Mental Health Support", page_icon=":brain:") # # # st.write(css, unsafe_allow_html=True) # # # if "conversation" not in st.session_state: # # # st.session_state.conversation = None # # # if "chat_history" not in st.session_state: # # # st.session_state.chat_history = None # # # st.header("Mental Health Support :brain:") # # # pdf_paths = [ # # # 'C:/Users/sharm/Downloads/ask-multiple-pdfs-main/ask-multiple-pdfs-main/Chat_data.pdf', # # # 'C:/Users/sharm/Downloads/ask-multiple-pdfs-main/ask-multiple-pdfs-main/class 10 history ch 3.pdf' # # # ] # # # # get pdf text # # # raw_text = get_pdf_text(pdf_paths) # # # # get the text chunks # # # text_chunks = get_text_chunks(raw_text) # # # # create vector store # # # vectorstore = get_vectorstore(text_chunks) # # # # create conversation chain # # # st.session_state.conversation = get_conversation_chain(vectorstore) # # # user_question = st.text_input("Your therapist is there for you!:") # # # if user_question and st.session_state.conversation: # # # handle_userinput(user_question) # # # if __name__ == '__main__': # # # main() import streamlit as st from dotenv import load_dotenv from PyPDF2 import PdfReader from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings,HuggingFaceInstructEmbeddings from langchain.vectorstores import FAISS from langchain.chat_models import ChatOpenAI from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationalRetrievalChain from langchain.llms import HuggingFaceHub from htmlTemplates import css, bot_template, user_template #from InstructorEmbedding import INSTRUCTOR import tempfile import ttsmms import soundfile as sf from streamlit.components.v1 import html def get_pdf_text(pdf_paths): text = "" for pdf_path in pdf_paths: with open(pdf_path, 'rb') as pdf_file: pdf_reader = PdfReader(pdf_file) for page in pdf_reader.pages: text += page.extract_text() return text def get_text_chunks(text): text_splitter = CharacterTextSplitter( separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len ) chunks = text_splitter.split_text(text) return chunks def get_vectorstore(text_chunks): #embeddings = OpenAIEmbeddings() embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-base") vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) return vectorstore def get_conversation_chain(vectorstore): llm = ChatOpenAI() memory = ConversationBufferMemory( memory_key='chat_history', return_messages=True) conversation_chain = ConversationalRetrievalChain.from_llm( llm=llm, retriever=vectorstore.as_retriever(), memory=memory ) return conversation_chain def handle_userinput(user_question): response = st.session_state.conversation({'question': user_question}) st.session_state.chat_history = response['chat_history'] for i, message in enumerate(st.session_state.chat_history): if i % 2 == 0: st.write(user_template.replace( "{{MSG}}", message.content), unsafe_allow_html=True) else: st.write(bot_template.replace( "{{MSG}}", message.content), unsafe_allow_html=True) audio_path = tempfile.NamedTemporaryFile(delete=False, suffix=".wav").name tts = ttsmms.TTS("data/eng") # Update with the correct path wav = tts.synthesis(response['answer']) sf.write(audio_path, wav["x"], wav["sampling_rate"]) st.audio(audio_path, format="audio/wav", start_time=0, sample_rate=wav["sampling_rate"]) def main(): load_dotenv() st.set_page_config(page_title="Mental Health Support", page_icon=":brain:") st.write(css, unsafe_allow_html=True) if "conversation" not in st.session_state: st.session_state.conversation = None if "chat_history" not in st.session_state: st.session_state.chat_history = None st.header("Mental Health Support :brain:") pdf_paths = [ 'Chat_data.pdf' ] raw_text = get_pdf_text(pdf_paths) text_chunks = get_text_chunks(raw_text) vectorstore = get_vectorstore(text_chunks) st.session_state.conversation = get_conversation_chain(vectorstore) user_question = st.text_input("Your therapist is there for you!:") if user_question and st.session_state.conversation: handle_userinput(user_question) if __name__ == '__main__': main() # my_js = """ # alert("Please don't forget to enter you daily details!!!"); # """ # # Wrapt the javascript as html code # my_html = f"" # # Execute your app # html(my_html)