delete copy app

NewMistral.py

@@ -1,141 +0,0 @@
-import streamlit as st
-from streamlit_chat import message
-from langchain.chains import ConversationalRetrievalChain
-from langchain.document_loaders import PyPDFLoader, DirectoryLoader
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.llms import CTransformers
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain.memory import ConversationBufferMemory
-import streamlit.components.v1 as components
-from templatesStreamlit import *
-import tempfile
-import os
-
-# Funcion para leer los documentos
-def load_documents(uploaded_files):
-    docs = []
-    temp_dir = tempfile.TemporaryDirectory()
-    for file in uploaded_files:
-        temp_filepath = os.path.join(temp_dir.name, file.name)
-        with open(temp_filepath, "wb") as f:
-            f.write(file.getvalue())
-        loader = PyPDFLoader(temp_filepath)
-        docs.extend(loader.load())
-
-    # loader = DirectoryLoader('data/', glob="*.pdf", loader_cls=PyPDFLoader)
-    # documents = loader.load()
-    return docs
-
-# Funcion para convertir el texto en chunks
-def split_text_into_chunks(documents):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-    text_chunks = text_splitter.split_documents(documents)
-    return text_chunks
-
-def get_vectorstore(text_chunks):
-    embbedings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': "cpu"})
-    vector_store = FAISS.from_documents(text_chunks, embbedings)
-    return vector_store
-
-# def create_llms_model():
-#     llm = CTransformers(model="mistral-7b-instruct-v0.1.Q4_K_M.gguf", config={'max_new_tokens': 512, 'temperature': 0.01})
-#     return llm
-
-def get_conversation_chain(vector_store):
-
-    llm = CTransformers(model="mistral-7b-instruct-v0.1.Q4_K_M.gguf", config={'max_new_tokens': 512, 'temperature': 0.01})
-    
-    #Creamos la memoria
-    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-
-    # Create chain (lANGCHAIN)
-    conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff',
-                                                retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
-                                                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_template2.replace(
-                "{{MSG}}", message.content), unsafe_allow_html=True)
-        else:
-            st.write(bot_template2.replace(
-                "{{MSG}}", message.content), unsafe_allow_html=True)
-
-
-def main():
-    st.set_page_config(page_title="LLM-RAG",
-                       page_icon=":books:")
-    st.write(css, unsafe_allow_html=True)
-
-    titulo = f"""
-     <div class="btn-neon">
-       <span class="icon"><img src=static/Mistral.png></span>
-        Mistral7b + Streamlit
-        <span class="icon"><img src=static/streamlit.png></span>
-     </div>
-    """
-    st.markdown(titulo, unsafe_allow_html=True)
-
-    presentacion = f"""
-            <div class="skill">
-                <div class="skill-content">
-                    <div class="skill-img-box">
-                        <a href="https://www.linkedin.com/in/manueloteromarquez/" target="_blank">
-                        <img src="https://media.licdn.com/dms/image/C4D03AQEsabRcMGkMmQ/profile-displayphoto-shrink_800_800/0/1663585925916?e=1708560000&v=beta&t=1Ofx1PsbTSlMcNIVCxznEjtIA_aIlTVaJm52toMKddU" alt="Tu descripción">
-                        </a>                    
-                    </div>
-                    <div class="skill-detail">
-                        <h2 class="skill-title">Manuel Otero</h2>
-                        <p>211 Days</p>
-                        <div class="skill-progress">
-                            <div class="progress progress-1"></div>
-                        </div>
-                    </div>
-                </div>
-                <h2 class="percent">60%</h2>
-            </div>
-"""
-    st.markdown(presentacion, 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("Hazle preguntas a tus documentos PDFs :books:")
-
-
-    with st.sidebar:
-        st.subheader("Tus Documentos")
-        pdf_docs = st.file_uploader(
-            "Sube tus PDFs aquí y pulsa 'Procesar PDF'", accept_multiple_files=True)
-        if not pdf_docs:
-            st.info("Sube tus pdfs para continuar.")
-            st.stop()
-            
-        if st.button("Procesar PDF"):
-            with st.spinner("Procesando"):
-                # get pdf text
-                documents = load_documents(pdf_docs)
-                print(documents)
-                text_chunks = split_text_into_chunks(documents)
-                # create vector store
-                vectorstore = get_vectorstore(text_chunks)
-                # create conversation chain
-                st.session_state.conversation = get_conversation_chain(vectorstore)
-    
-    user_question = st.text_input("Adelante pregunta")
-    if user_question:
-        handle_userinput(user_question)
-
-
-
-
-if __name__ == '__main__':
-    main()