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import os
import sys
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import PyPDFLoader, Docx2txtLoader, TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from sentence_transformers import SentenceTransformer
import torch
# sqlite workaround for HuggingFace Spaces
__import__('pysqlite3')
sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
# Load documents
docs = []
for f in os.listdir("multiple_docs"):
if f.endswith(".pdf"):
loader = PyPDFLoader(os.path.join("multiple_docs", f))
docs.extend(loader.load())
elif f.endswith(".docx") or f.endswith(".doc"):
loader = Docx2txtLoader(os.path.join("multiple_docs", f))
docs.extend(loader.load())
elif f.endswith(".txt"):
loader = TextLoader(os.path.join("multiple_docs", f))
docs.extend(loader.load())
# Split docs
splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=10)
docs = splitter.split_documents(docs)
# Embeddings
embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
texts = [doc.page_content for doc in docs]
metadatas = [{"id": i} for i in range(len(texts))]
embeddings = embedding_model.encode(texts)
# Vectorstore
vectorstore = Chroma(persist_directory="./db")
vectorstore.add_texts(texts=texts, metadatas=metadatas, embeddings=embeddings)
vectorstore.persist()
# model_name = "deepseek-ai/deepseek-llm-7b-instruct"
# tokenizer = AutoTokenizer.from_pretrained(model_name)
# model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
model_name = "google/flan-t5-large"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def generate(prompt):
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
class HuggingFaceLLMWrapper:
def __call__(self, prompt, **kwargs):
return generate(prompt)
llm = HuggingFaceLLMWrapper()
# QA chain
chain = ConversationalRetrievalChain.from_llm(
llm,
retriever=vectorstore.as_retriever(search_kwargs={'k': 6}),
return_source_documents=True,
verbose=False
)
chat_history = []
with gr.Blocks() as demo:
chatbot = gr.Chatbot([("", "Hello, I'm Thierry Decae's chatbot. Ask me about my experience, skills, eligibility, etc.")],
avatar_images=["./multiple_docs/Guest.jpg", "./multiple_docs/Thierry Picture.jpg"])
msg = gr.Textbox()
clear = gr.Button("Clear")
def user(query, chat_history):
chat_history_tuples = [(m[0], m[1]) for m in chat_history]
result = chain({"question": query, "chat_history": chat_history_tuples})
chat_history.append((query, result["answer"]))
return gr.update(value=""), chat_history
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False)
clear.click(lambda: None, None, chatbot, queue=False)
demo.launch(debug=True)
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