app.py

import gradio as gr
import os
from llama_parse import LlamaParse
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.schema import Document as LangchainDocument

# Initialize global variables
vs_dict = {}

# Helper function to load and parse the input data
def mariela_parse(files):
    parser = LlamaParse(
        api_key=os.getenv("LLAMA_API_KEY"),
        result_type="markdown",
        verbose=True
    )
    parsed_documents = []
    for file in files:
        parsed_documents.extend(parser.load_data(file.name))
    return parsed_documents

# Create vector database
def mariela_create_vector_database(parsed_documents, collection_name):
    langchain_docs = [
        LangchainDocument(page_content=doc.text, metadata=doc.metadata)
        for doc in parsed_documents
    ]

    text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=100)
    docs = text_splitter.split_documents(langchain_docs)

    embed_model = FastEmbedEmbeddings(model_name="BAAI/bge-base-en-v1.5")

    vs = Chroma.from_documents(
        documents=docs,
        embedding=embed_model,
        persist_directory="chroma_db",
        collection_name=collection_name
    )

    return vs

# Function to handle file upload and parsing
def mariela_upload_and_parse(files, collection_name):
    global vs_dict
    if not files:
        return "Please upload at least one file."

    parsed_documents = mariela_parse(files)
    vs = mariela_create_vector_database(parsed_documents, collection_name)

    vs_dict[collection_name] = vs

    return f"Files uploaded, parsed, and stored successfully in collection: {collection_name}"

# Function to handle retrieval
def mariela_retrieve(question, collection_name):
    global vs_dict
    if collection_name not in vs_dict:
        return f"Collection '{collection_name}' not found. Please upload and parse files for this collection first."

    vs = vs_dict[collection_name]
    results = vs.similarity_search(question, k=12)
    
    formatted_results = []
    for i, doc in enumerate(results, 1):
        formatted_results.append(f"Result {i}:\n{doc.page_content}\n\nMetadata: {doc.metadata}\n")
    
    return "\n\n".join(formatted_results)

# Supported file types list
supported_file_types = """
Supported Document Types:
- Base types: pdf
- Documents and presentations: 602, abw, cgm, cwk, doc, docx, docm, dot, dotm, hwp, key, lwp, mw, mcw, pages, pbd, ppt, pptm, pptx, pot, potm, potx, rtf, sda, sdd, sdp, sdw, sgl, sti, sxi, sxw, stw, sxg, txt, uof, uop, uot, vor, wpd, wps, xml, zabw, epub
- Images: jpg, jpeg, png, gif, bmp, svg, tiff, webp, web, htm, html
- Spreadsheets: xlsx, xls, xlsm, xlsb, xlw, csv, dif, sylk, slk, prn, numbers, et, ods, fods, uos1, uos2, dbf, wk1, wk2, wk3, wk4, wks, 123, wq1, wq2, wb1, wb2, wb3, qpw, xlr, eth, tsv
"""

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Mariela: Multi-Action Retrieval and Intelligent Extraction Learning Assistant")
    gr.Markdown("This application allows you to upload documents, parse them, and then ask questions to retrieve relevant information.")

    with gr.Tab("Upload and Parse Files"):
        gr.Markdown("## Upload and Parse Files")
        gr.Markdown("Upload your documents here to create a searchable knowledge base.")
        file_input = gr.File(label="Upload Files", file_count="multiple")
        collection_name_input = gr.Textbox(label="Collection Name")
        upload_button = gr.Button("Upload and Parse")
        upload_output = gr.Textbox(label="Status")

        upload_button.click(mariela_upload_and_parse, inputs=[file_input, collection_name_input], outputs=upload_output)

    with gr.Tab("Retrieval"):
        gr.Markdown("## Retrieval")
        gr.Markdown("Ask questions about your uploaded documents here.")
        collection_name_retrieval = gr.Textbox(label="Collection Name")
        question_input = gr.Textbox(label="Enter a query to retrieve relevant passages")
        retrieval_output = gr.Textbox(label="Retrieved Passages")
        retrieval_button = gr.Button("Retrieve")

        retrieval_button.click(mariela_retrieve, inputs=[question_input, collection_name_retrieval], outputs=retrieval_output)
    
    with gr.Tab("Supported Document Types"):
        gr.Markdown("## Supported Document Types")
        gr.Markdown(supported_file_types)

demo.launch(debug=True)