embeddings

app.py

@@ -1,110 +1,7 @@
-import os
-import time
-import streamlit as st
-from htmlTemplates import css, bot_template, user_template
+import Streamlit as st
 from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import Chroma
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import RetrievalQA
-from pdfminer.high_level import extract_text
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from langchain.vectorstores import FAISS
 
-
-# Updated Prompt Template
-persist_directory = 'db'
-embeddings_model_name = 'sentence-transformers/all-MiniLM-L6-v2'
-
-def get_pdf_text(pdf_path):
-    return extract_text(pdf_path)
-
-def get_pdf_text_chunks(pdf_text):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
-    return text_splitter.split_text(text=pdf_text)
-
-def create_vector_store(target_source_chunks):
-    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
-    db = Chroma.from_texts(texts=target_source_chunks, persist_directory=persist_directory, embedding=embeddings)
-    db.persist()
-    return db
-
-def get_vector_store(target_source_chunks):
-    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
-    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
-    retriver = db.as_retriever(search_kwargs={"k": target_source_chunks})
-    return retriver
-
-def get_conversation_chain(retriever):
-    tokenizer = AutoTokenizer.from_pretrained("TinyPixel/Llama-2-7B-bf16-sharded")
-    model = AutoModelForCausalLM.from_pretrained("TinyPixel/Llama-2-7B-bf16-sharded")
-    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True,)
-    chain = RetrievalQA.from_llm(
-        llm=model,
-        memory=memory,
-        retriever=retriever,
-    )
-    return chain
-
-
-def handle_userinput(user_question):
-    if st.session_state.conversation is None:
-        st.warning("Please load the Vectorstore first!")
-        return
-    else:
-        with st.spinner('Thinking...', ):
-            start_time = time.time()
-            response = st.session_state.conversation({'query': user_question})
-            end_time = time.time()
-
-            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)
-
-            st.write('Elapsed time: {:.2f} seconds'.format(end_time - start_time))
-            st.balloons()
-
-
-
-
-def main():
-
-    st.set_page_config(page_title='Java Copilot :coffee:', page_icon=':rocket:', layout='wide', )
-    with st.sidebar.title(':gear: Parameters'):
-        model_n_ctx = st.sidebar.slider('Model N_CTX', min_value=128, max_value=2048, value=1024, step=2)
-        model_n_batch = st.sidebar.slider('Model N_BATCH', min_value=1, max_value=model_n_ctx, value=512, step=2)
-        target_source_chunks = st.sidebar.slider('Target Source Chunks', min_value=1, max_value=10, value=4, step=1)
-    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('Java Copilot :coffee:')
-    st.subheader('Upload your PDF file and start chatting with it!')
-    user_question = st.text_input('Enter your message here:')
-    pdf_file = st.file_uploader("Upload PDF", type=['pdf'])
-    if st.button('Start Chain'):
-        if pdf_file is not None:
-            with st.spinner('Working in progress ...'):
-                pdf_text = get_pdf_text(pdf_file)
-                pdf_text_chunks = get_pdf_text_chunks(pdf_text)
-                st.session_state.vector_store = create_vector_store(pdf_text_chunks)
-                st.session_state.conversation = get_conversation_chain(
-                retriever=st.session_state.vector_store,
-                )
-        st.success('Vectorstore created successfully! You can start chatting now!')
-    else:
-        st.warning('Please upload a PDF file first!')
-
-
-    if user_question:
-        handle_userinput(user_question)
-
-
-if __name__ == '__main__':
-    main()
+st.title("Embedding Creation for Langchain")
+st.header("This is a header")
+files = st.file_uploader("Upload your files", accept_multiple_files=True, type="pdf")