الحل: بناء نظام ضمان الجودة لملفات متعددة

import streamlit as st
from langchain import hub
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
import fitz  # PyMuPDF

# Specify the filename of your local image
image_filename = 'Educative.png'

# Use st.image to display the image
st.image(image_filename, use_column_width=True)

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

def read_pdf(file):
    pdf_document = fitz.open(stream=file.read(), filetype="pdf")
    text = ""
    for page_num in range(pdf_document.page_count):
        page = pdf_document.load_page(page_num)
        text += page.get_text()
    return text

def generate_response(uploaded_files, openai_api_key, query_text):
    documents = []
    for uploaded_file in uploaded_files:
        # Check if the file is a PDF
        if uploaded_file.type == "application/pdf":
            document_text = read_pdf(uploaded_file)
        else:
            document_text = uploaded_file.read().decode()
        documents.append(document_text)

    # Split documents into chunks
    text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=100)
    texts = []
    for document in documents:
        texts.extend(text_splitter.create_documents([document]))
    
    llm = ChatOpenAI(model="gpt-4o", openai_api_key=openai_api_key)
    # Select embeddings
    embeddings = OpenAIEmbeddings(model="text-embedding-3-small", openai_api_key=openai_api_key)
    # Create a vector store from documents
    database = Chroma.from_documents(texts, embeddings)
    # Create retriever interface
    retriever = database.as_retriever()
    prompt = hub.pull("rlm/rag-prompt")
    rag_chain = (
        {"context": retriever | format_docs, "question": RunnablePassthrough()}
        | prompt
        | llm
        | StrOutputParser()
    )
    # Create QA chain
    response = rag_chain.invoke(query_text)
    return response

# File upload
uploaded_files = st.file_uploader('Upload one or more articles', type=['txt', 'pdf'], accept_multiple_files=True)
# Query text
query_text = st.text_input('Enter your question:', placeholder='Please provide a short summary.', disabled=not uploaded_files)

# Form input and query
result = None
with st.form('myform', clear_on_submit=False, border=False):
    openai_api_key = st.text_input('OpenAI API Key', type='password', disabled=not (uploaded_files and query_text))
    submitted = st.form_submit_button('Submit', disabled=not(uploaded_files and query_text))
    if submitted and openai_api_key.startswith('sk-'):
        with st.spinner('Calculating...'):
            response = generate_response(uploaded_files, openai_api_key, query_text)
            result = response
if result:
    st.info(result)