# import google.generativeai as palm import streamlit as st # import os # # Set your API key # palm.configure(api_key = os.environ['PALM_KEY']) # # Select the PaLM 2 model # model = 'models/text-bison-001' # # Generate text # if prompt := st.chat_input("Ask your query..."): # enprom = f"""Act as bhagwan krishna and Answer the below provided input in context to Bhagwad Geeta. Use the verses and chapters sentences as references to your answer with suggestions # coming from Bhagwad Geeta. Your answer to below input should only be in context to Bhagwad geeta.\nInput= {prompt}""" # completion = palm.generate_text(model=model, prompt=enprom, temperature=0.5, max_output_tokens=800) # # response = palm.chat(messages=["Hello."]) # # print(response.last) # 'Hello! What can I help you with?' # # response.reply("Can you tell me a joke?") # # Print the generated text # with st.chat_message("Assistant"): # st.write(prompt) # st.write(completion.result) from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**input_ids) st.write(tokenizer.decode(outputs[0])) # import streamlit as st # from dotenv import load_dotenv # from PyPDF2 import PdfReader # from langchain.text_splitter import CharacterTextSplitter # from langchain.embeddings import HuggingFaceEmbeddings # 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 # import os # # from transformers import T5Tokenizer, T5ForConditionalGeneration # # from langchain.callbacks import get_openai_callback # hub_token = os.environ["HUGGINGFACE_HUB_TOKEN"] # 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=200, # chunk_overlap=20, # 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") # embeddings = HuggingFaceEmbeddings() # vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) # return vectorstore # def get_conversation_chain(vectorstore): # # llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k") # # tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base") # # model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base") # llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-v0.1", huggingfacehub_api_token=hub_token, model_kwargs={"temperature":0.5, "max_length":20}) # 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 # reply = response.run(user_question) # st.write(reply) # # 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="Chat with multiple PDFs", # 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("Chat with multiple PDFs :books:") # 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"): # if(len(pdf_docs) == 0): # st.error("Please upload at least one PDF") # else: # 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 os # # import getpass # # import streamlit as st # # from langchain.document_loaders import PyPDFLoader # # from langchain.text_splitter import RecursiveCharacterTextSplitter # # from langchain.embeddings import HuggingFaceEmbeddings # # from langchain.vectorstores import Chroma # # from langchain import HuggingFaceHub # # from langchain.chains import RetrievalQA # # # __import__('pysqlite3') # # # import sys # # # sys.modules['sqlite3'] = sys.modules.pop('pysqlite3') # # # load huggingface api key # # hubtok = os.environ["HUGGINGFACE_HUB_TOKEN"] # # # use streamlit file uploader to ask user for file # # # file = st.file_uploader("Upload PDF") # # path = "Geeta.pdf" # # loader = PyPDFLoader(path) # # pages = loader.load() # # # st.write(pages) # # splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20) # # docs = splitter.split_documents(pages) # # embeddings = HuggingFaceEmbeddings() # # doc_search = Chroma.from_documents(docs, embeddings) # # repo_id = "tiiuae/falcon-7b" # # llm = HuggingFaceHub(repo_id=repo_id, huggingfacehub_api_token=hubtok, model_kwargs={'temperature': 0.2,'max_length': 1000}) # # from langchain.schema import retriever # # retireval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=doc_search.as_retriever()) # # if query := st.chat_input("Enter a question: "): # # with st.chat_message("assistant"): # # st.write(retireval_chain.run(query))