Added app.py file

app.py

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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 = [
+        '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'
+    ]
+    
+    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)