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d31ac17 464fb2b d31ac17 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 | # # # 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) |