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import os
import gradio as gr
from langchain_huggingface import HuggingFaceEndpoint,HuggingFaceEmbeddings,ChatHuggingFace
from langchain_core.load import dumpd, dumps, load, loads
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.callbacks import StreamingStdOutCallbackHandler
from langchain_chroma import Chroma
from langchain_core.documents import Document
from langchain_text_splitters import CharacterTextSplitter
from pypdf import PdfReader
import random
cwd = os.getcwd()
print(cwd)
token=""
#repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
emb = "sentence-transformers/all-mpnet-base-v2"
hf = HuggingFaceEmbeddings(model_name=emb)
#db = Chroma(persist_directory=f"{cwd}/chroma_langchain_db",embedding_function=HuggingFaceEmbeddings(model_name=emb))
#db.persist()
# Load the document, split it into chunks, embed each chunk and load it into the vector store.
#raw_documents = TextLoader('state_of_the_union.txt').load()
def embed_fn(inp):
db=Chroma()
text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=10)
documents = text_splitter.split_text(inp)
out_emb= hf.embed_documents(documents)
string_representation = dumps(out_emb, pretty=True)
db.from_texts(documents,embedding_function=HuggingFaceEmbeddings(model_name=emb),persist_directory=f"{cwd}/chroma_langchain_db")
def proc_doc(doc_in):
for doc in doc_in:
if doc.endswith(".txt"):
yield [["",f"Loading Document: {doc}"]]
outp = read_txt(doc)
embed_fn(outp)
yield [["","Loaded"]]
elif doc.endswith(".pdf"):
yield [["",f"Loading Document: {doc}"]]
outp = read_pdf(doc)
embed_fn(outp)
yield [["","Loaded"]]
def read_txt(txt_path):
text=""
with open(txt_path,"r") as f:
text = f.read()
f.close()
return text
def read_pdf(pdf_path):
text=""
reader = PdfReader(f'{pdf_path}')
number_of_pages = len(reader.pages)
for i in range(number_of_pages):
page = reader.pages[i]
text = f'{text}\n{page.extract_text()}'
return text
def run_llm(input_text,history):
MAX_TOKENS=20000
try:
qur= hf.embed_query(input_text)
docs = db.similarity_search_by_vector(qur, k=3)
print(docs)
except Exception as e:
print(e)
callbacks = [StreamingStdOutCallbackHandler()]
llm = HuggingFaceEndpoint(
endpoint_url=repo_id,
max_new_tokens=2056,
seed=random.randint(1,99999999999),
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=0.01,
repetition_penalty=1.03,
#callbacks=callbacks,
streaming=True,
huggingfacehub_api_token=token,
)
out=""
#prompt = ChatPromptTemplate.from_messages(
prompt=[
{"role": "system", "content": f"[INST] Use this data to help answer users questions: {str(docs)} [/INST]"},
{"role": "user", "content": f"[INST]{input_text}[/INST]"},
]
t=llm.invoke(prompt)
for chunk in t:
out+=chunk
yield out
css="""
#component-0 {
height:400px;
}
"""
with gr.Blocks(css=css) as app:
data=gr.State()
with gr.Column():
#input_text = gr.Textbox(label="You: ")
chat = gr.ChatInterface(
fn=run_llm,
type="tuples",
concurrency_limit=20,
)
with gr.Row():
msg=gr.HTML()
file_in=gr.Files(file_count="multiple")
file_in.change(proc_doc, file_in, msg)
#btn = gr.Button("Generate")
app.queue().launch() |