import gradio as gr
import os
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import pipeline
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.vectorstores import Chroma
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
from langchain.chains import RetrievalQA
from langchain.document_loaders import PDFMinerLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
import chromadb
import tempfile

# Define Chroma Settings
CHROMA_SETTINGS = {
    "chroma_db_impl": "duckdb+parquet",
    "persist_directory": tempfile.mkdtemp(),  # Use a temporary directory
    "anonymized_telemetry": False
}

# Load model and tokenizer
checkpoint = "MBZUAI/LaMini-Flan-T5-783M"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
base_model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, device_map=torch.device("cpu"), torch_dtype=torch.float32)

# Define functions
def data_ingestion(file_path):
    loader = PDFMinerLoader(file_path)
    documents = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=500)
    texts = text_splitter.split_documents(documents)
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    db = Chroma.from_documents(texts, embeddings, persist_directory=CHROMA_SETTINGS["persist_directory"])
    db.persist()
    print(texts)
    return db

def llm_pipeline():
    pipe = pipeline(
        "text2text-generation",
        model=base_model,
        tokenizer=tokenizer,
        max_length=256,
        do_sample=True,
        temperature=0.3,
        top_p=0.95
    )
    local_llm = HuggingFacePipeline(pipeline=pipe)
    return local_llm

def qa_llm():
    llm = llm_pipeline()
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    vectordb = Chroma(persist_directory=CHROMA_SETTINGS["persist_directory"], embedding_function=embeddings)
    retriever = vectordb.as_retriever()
    qa = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=True
    )
    
    return qa

def process_answer(file, instruction):
    # Ingest the data from the uploaded PDF
    data_ingestion(file.name)
    # Process the question
    qa = qa_llm()
    generated_text = qa(instruction)
    answer = generated_text["result"]
    return answer

# Define Gradio interfac
iface = gr.Interface(
    fn=process_answer, 
    inputs=["file", "text"], 
    outputs="text"
)

# Launch the interface
iface.launch()