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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() |