first commit

app.py.py

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+from dotenv import load_dotenv
+import os
+import streamlit as st
+from PyPDF2 import PdfFileReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI as LLMSOpenAI
+from langchain.llms import AzureOpenAI
+from langchain.callbacks import get_openai_callback
+from langchain.chat_models import ChatOpenAI
+from docx import Document
+from openpyxl import load_workbook
+import pdfplumber
+
+
+def extract_text_from_pdf(pdf_file):
+    with pdfplumber.open(pdf_file) as pdf:
+        text = ""
+        for page in pdf.pages:
+            text += page.extract_text()
+    return text
+
+
+def extract_text_from_docx(docx_file):
+    doc = Document(docx_file)
+    paragraphs = [paragraph.text for paragraph in doc.paragraphs]
+    return "\n".join(paragraphs)
+
+
+def extract_text_from_excel(excel_file):
+    workbook = load_workbook(excel_file)
+    text = ""
+    for sheet in workbook.sheetnames:
+        worksheet = workbook[sheet]
+        for row in worksheet.iter_rows():
+            for cell in row:
+                if cell.value:
+                    text += str(cell.value) + "\n"
+    return text
+
+
+def split_text_into_chunks(text):
+    text_splitter = CharacterTextSplitter(
+        separator="\n",
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len
+    )
+    return text_splitter.split_text(text)
+
+
+def create_knowledge_base(chunks, api_key=None):
+    embeddings = OpenAIEmbeddings(openai_api_key=api_key)
+    knowledge_base = FAISS.from_texts(chunks, embeddings)
+    return knowledge_base
+
+
+def answer_question(question, knowledge_base, model):
+    docs = knowledge_base.similarity_search(question)
+    llm = model(model_name="gpt-3.5-turbo")
+    chain = load_qa_chain(llm, chain_type="stuff")
+    with get_openai_callback() as cb:
+        response = chain.run(input_documents=docs, question=question)
+    return response
+
+
+def save_api_key(api_key):
+    st.session_state.api_key = api_key
+
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="Ask Your PDF", layout="wide")
+
+    # Sidebar
+    st.sidebar.title("Settings")
+
+    # API Key input
+    st.sidebar.subheader("API Key")
+    api_key = st.sidebar.text_input("Insert your API Key", type="password")
+    st.sidebar.button("Save API Key", on_click=save_api_key, args=(api_key,))
+
+    model_type = st.sidebar.selectbox("Select Language Model", ["OpenAI", "AzureOpenAI"])
+    if model_type == "AzureOpenAI":
+        model = AzureOpenAI
+    else:
+        model = ChatOpenAI
+
+    chunk_size = st.sidebar.slider("Chunk Size", min_value=500, max_value=2000, value=1000, step=100)
+    chunk_overlap = st.sidebar.slider("Chunk Overlap", min_value=100, max_value=500, value=200, step=50)
+    show_content = st.sidebar.checkbox("Show Document Content")
+    show_answers = st.sidebar.checkbox("Show Previous Answers")
+
+    # Main content
+    st.title("Ask Your Document 💭")
+    file_format = st.selectbox("Select File Format", ["PDF", "docx", "xlsx"])
+    document = st.file_uploader("Upload Document", type=[file_format.lower()])
+
+    if not hasattr(st.session_state, "api_key") or not st.session_state.api_key:
+        st.warning("You need to insert your API Key first.")
+    elif document is not None:
+        if file_format == "PDF":
+            text = extract_text_from_pdf(document)
+        elif file_format == "docx":
+            text = extract_text_from_docx(document)
+        elif file_format == "xlsx":
+            text = extract_text_from_excel(document)
+        else:
+            text = ""
+
+        if show_content:
+            st.subheader("Document Text:")
+            st.text_area("Content", value=text, height=300)
+
+        chunks = split_text_into_chunks(text)
+        knowledge_base = create_knowledge_base(chunks, api_key=st.session_state.api_key)
+
+        user_question = st.text_input("Ask a question based on the document content:")
+
+        if user_question:
+            response = answer_question(user_question, knowledge_base, model)
+            st.subheader("Answer:")
+            st.write(response)
+
+            # Store and display previous answers
+            if "answers" not in st.session_state:
+                st.session_state.answers = []
+            st.session_state.answers.append((user_question, response))
+
+        if show_answers:
+            st.subheader("Previous Answers:")
+            for question, answer in st.session_state.answers:
+                st.write(f"Question: {question}")
+                st.write(f"Answer: {answer}")
+                st.write("------")
+
+
+if __name__ == '__main__':
+    main()
+

requirements.txt

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+dotenv
+os
+streamlit 
+PyPDF2
+langchain.text_splitter
+langchain.embeddings.openai 
+langchain.vectorstores 
+langchain.chains.question_answering 
+langchain.llms 
+langchain.callbacks 
+langchain.chat_models 
+docx 
+openpyxl 
+pdfplumber