Delete app1.py

app1.py

@@ -1,126 +0,0 @@
-import streamlit as st
-from streamlit_chat import message
-from langchain.chains import ConversationalRetrievalChain
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.llms import CTransformers
-from langchain.llms import Replicate
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain.memory import ConversationBufferMemory
-from langchain.document_loaders import PyPDFLoader
-from langchain.document_loaders import TextLoader
-from langchain.document_loaders import Docx2txtLoader
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-import os
-from dotenv import load_dotenv
-import tempfile
-
-
-load_dotenv()
-
-
-def initialize_session_state():
-    if 'history' not in st.session_state:
-        st.session_state['history'] = []
-
-    if 'generated' not in st.session_state:
-        st.session_state['generated'] = ["Hello! Ask me anything about 🤗"]
-
-    if 'past' not in st.session_state:
-        st.session_state['past'] = ["Hey! 👋"]
-
-def conversation_chat(query, chain, history):
-    result = chain({"question": query, "chat_history": history})
-    history.append((query, result["answer"]))
-    return result["answer"]
-
-def display_chat_history(chain):
-    reply_container = st.container()
-    container = st.container()
-
-    with container:
-        with st.form(key='my_form', clear_on_submit=True):
-            user_input = st.text_input("Question:", placeholder="Ask about your Documents", key='input')
-            submit_button = st.form_submit_button(label='Send')
-
-        if submit_button and user_input:
-            with st.spinner('Generating response...'):
-                output = conversation_chat(user_input, chain, st.session_state['history'])
-
-            st.session_state['past'].append(user_input)
-            st.session_state['generated'].append(output)
-
-    if st.session_state['generated']:
-        with reply_container:
-            for i in range(len(st.session_state['generated'])):
-                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="thumbs")
-                message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")
-
-def create_conversational_chain(vector_store):
-    load_dotenv()
-    # Create llm
-    #llm = CTransformers(model="llama-2-7b-chat.ggmlv3.q4_0.bin",
-                        #streaming=True, 
-                        #callbacks=[StreamingStdOutCallbackHandler()],
-                        #model_type="llama", config={'max_new_tokens': 500, 'temperature': 0.01})
-    llm = Replicate(
-        streaming = True,
-        model = "replicate/llama-2-70b-chat:58d078176e02c219e11eb4da5a02a7830a283b14cf8f94537af893ccff5ee781", 
-        callbacks=[StreamingStdOutCallbackHandler()],
-        input = {"temperature": 0.01, "max_length" :500,"top_p":1})
-    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-
-    chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff',
-                                                 retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
-                                                 memory=memory)
-    return chain
-
-def main():
-    load_dotenv()
-    # Initialize session state
-    initialize_session_state()
-    st.title("Multi-Docs ChatBot using llama-2-70b :books:")
-    # Initialize Streamlit
-    st.sidebar.title("Document Processing")
-    uploaded_files = st.sidebar.file_uploader("Upload files", accept_multiple_files=True)
-
-
-    if uploaded_files:
-        text = []
-        for file in uploaded_files:
-            file_extension = os.path.splitext(file.name)[1]
-            with tempfile.NamedTemporaryFile(delete=False) as temp_file:
-                temp_file.write(file.read())
-                temp_file_path = temp_file.name
-
-            loader = None
-            if file_extension == ".pdf":
-                loader = PyPDFLoader(temp_file_path)
-            elif file_extension == ".docx" or file_extension == ".doc":
-                loader = Docx2txtLoader(temp_file_path)
-            elif file_extension == ".txt":
-                loader = TextLoader(temp_file_path)
-
-            if loader:
-                text.extend(loader.load())
-                os.remove(temp_file_path)
-
-        text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=100, length_function=len)
-        text_chunks = text_splitter.split_documents(text)
-
-        # Create embeddings
-        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", 
-                                           model_kwargs={'device': 'cpu'})
-
-        # Create vector store
-        vector_store = FAISS.from_documents(text_chunks, embedding=embeddings)
-
-        # Create the chain object
-        chain = create_conversational_chain(vector_store)
-
-        
-        display_chat_history(chain)
-
-if __name__ == "__main__":
-    main()
-