app.py

import os
import json
import gradio as gr
import pandas as pd
from tempfile import NamedTemporaryFile
from typing import List
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
from langchain_core.output_parsers import StrOutputParser
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.llms import HuggingFaceHub
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_core.documents import Document
huggingface_token = os.environ.get("HUGGINGFACE_TOKEN")

# Memory database to store question-answer pairs
memory_database = {}
import os
import json
import gradio as gr
import pandas as pd
from tempfile import NamedTemporaryFile
from typing import List

from langchain_core.prompts import ChatPromptTemplate
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
from langchain_core.output_parsers import StrOutputParser
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceHub
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_core.text_splitters import RecursiveCharacterTextSplitter
from langchain_core.document import Document

huggingface_token = os.environ.get("HUGGINGFACE_TOKEN")

# Memory database to store question-answer pairs
memory_database = {}

def load_and_split_document_basic(file):
    """Loads and splits the document into pages."""
    loader = PyPDFLoader(file.name)
    data = loader.load_and_split()
    return data

def load_and_split_document_recursive(file: NamedTemporaryFile) -> List[Document]:
    """Loads and splits the document into chunks."""
    loader = PyPDFLoader(file.name)
    pages = loader.load()
    
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len,
    )
    
    chunks = text_splitter.split_documents(pages)
    return chunks

def get_embeddings():
    return HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

def create_or_update_database(data, embeddings):
    if os.path.exists("faiss_database"):
        db = FAISS.load_local("faiss_database", embeddings, allow_dangerous_deserialization=True)
        db.add_documents(data)
    else:
        db = FAISS.from_documents(data, embeddings)
    db.save_local("faiss_database")

def clear_cache():
    if os.path.exists("faiss_database"):
        os.remove("faiss_database")
        return "Cache cleared successfully."
    else:
        return "No cache to clear."

prompt = """
Answer the question based only on the following context:
{context}
Question: {question}

Provide a concise and direct answer to the question:
"""

def get_model(temperature, top_p, repetition_penalty):
    return HuggingFaceHub(
        repo_id="mistralai/Mistral-7B-Instruct-v0.3",
        model_kwargs={
            "temperature": temperature,
            "top_p": top_p,
            "repetition_penalty": repetition_penalty,
            "max_length": 512
        },
        huggingfacehub_api_token=huggingface_token
    )

def generate_chunked_response(model, prompt, max_tokens=500, max_chunks=5):
    full_response = ""
    for i in range(max_chunks):
        chunk = model(prompt + full_response, max_new_tokens=max_tokens)
        full_response += chunk
        if chunk.strip().endswith((".", "!", "?")):
            break
    return full_response.strip()

def response(database, model, question):
    prompt_val = ChatPromptTemplate.from_template(prompt)
    retriever = database.as_retriever()
    
    context = retriever.get_relevant_documents(question)
    context_str = "\n".join([doc.page_content for doc in context])
    
    formatted_prompt = prompt_val.format(context=context_str, question=question)
    
    ans = generate_chunked_response(model, formatted_prompt)
    return ans  # Only return the answer

def update_vectors(files, use_recursive_splitter):
    if not files:
        return "Please upload at least one PDF file."
    
    embed = get_embeddings()
    total_chunks = 0
    
    for file in files:
        if use_recursive_splitter:
            data = load_and_split_document_recursive(file)
        else:
            data = load_and_split_document_basic(file)
        create_or_update_database(data, embed)
        total_chunks += len(data)
    
    return f"Vector store updated successfully. Processed {total_chunks} chunks from {len(files)} files."

def ask_question(question, temperature, top_p, repetition_penalty):
    if not question:
        return "Please enter a question."
    
    # Check if the question exists in the memory database
    if question in memory_database:
        return memory_database[question]
    
    embed = get_embeddings()
    database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
    model = get_model(temperature, top_p, repetition_penalty)
    
    # Generate response from document database
    answer = response(database, model, question)
    
    # Store the question and answer in the memory database
    memory_database[question] = answer
    
    return answer

def extract_db_to_excel():
    embed = get_embeddings()
    database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
    
    documents = database.docstore._dict.values()
    data = [{"page_content": doc.page_content, "metadata": json.dumps(doc.metadata)} for doc in documents]
    df = pd.DataFrame(data)
    
    with NamedTemporaryFile(delete=False, suffix='.xlsx') as tmp:
        excel_path = tmp.name
        df.to_excel(excel_path, index=False)
    
    return excel_path

def export_memory_db_to_excel():
    data = [{"question": question, "answer": answer} for question, answer in memory_database.items()]
    df = pd.DataFrame(data)
    
    with NamedTemporaryFile(delete=False, suffix='.xlsx') as tmp:
        excel_path = tmp.name
        df.to_excel(excel_path, index=False)
    
    return excel_path

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Chat with your PDF documents")
    
    with gr.Row():
        file_input = gr.Files(label="Upload your PDF documents", file_types=[".pdf"])
        update_button = gr.Button("Update Vector Store")
        use_recursive_splitter = gr.Checkbox(label="Use Recursive Text Splitter", value=False)
    
    update_output = gr.Textbox(label="Update Status")
    update_button.click(update_vectors, inputs=[file_input, use_recursive_splitter], outputs=update_output)
    
    with gr.Row():
        question_input = gr.Textbox(label="Ask a question about your documents")
        temperature_slider = gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, value=0.5, step=0.1)
        top_p_slider = gr.Slider(label="Top P", minimum=0.0, maximum=1.0, value=0.9, step=0.1)
        repetition_penalty_slider = gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.0, step=0.1)
        submit_button = gr.Button("Submit")
    
    answer_output = gr.Textbox(label="Answer")
    submit_button.click(ask_question, inputs=[question_input, temperature_slider, top_p_slider, repetition_penalty_slider], outputs=answer_output)
    
    extract_button = gr.Button("Extract Database to Excel")
    excel_output = gr.File(label="Download Excel File")
    extract_button.click(extract_db_to_excel, inputs=[], outputs=excel_output)
    
    export_memory_button = gr.Button("Export Memory Database to Excel")
    memory_excel_output = gr.File(label="Download Memory Excel File")
    export_memory_button.click(export_memory_db_to_excel, inputs=[], outputs=memory_excel_output)
    
    clear_button = gr.Button("Clear Cache")
    clear_output = gr.Textbox(label="Cache Status")
    clear_button.click(clear_cache, inputs=[], outputs=clear_output)

if __name__ == "__main__":
    demo.launch()