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| # # # 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"<script>{my_js}</script>" | |
| # # Execute your app | |
| # html(my_html) |