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import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_huggingface import HuggingFacePipeline
from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA
import gradio as gr
import spaces
# Load TinyLlama model
model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
# Create a text generation pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
# Wrap the pipeline in a LangChain HuggingFacePipeline
llm = HuggingFacePipeline(pipeline=pipe)
# Load embeddings
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
# Load the FAISS index
db_FAISS = FAISS.load_local("/home/user/app/", embeddings, allow_dangerous_deserialization=True)
# Create a RetrievalQA chain
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=db_FAISS.as_retriever(search_kwargs={"k": 3}),
return_source_documents=True
)
print("fuck14")
@spaces.GPU
def query_documents(query):
result = qa_chain({"query": query})
answer = result['result']
sources = [doc.metadata for doc in result['source_documents']]
return answer, sources
# Gradio interface
def gradio_interface(query):
answer, sources = query_documents(query)
source_text = "\n\nSources:\n" + "\n".join([f"Source: {s.get('source', 'Unknown')}, Page: {s.get('page', 'Unknown')}" for s in sources])
return answer + source_text
iface = gr.Interface(
fn=gradio_interface,
inputs="text",
outputs="text",
title="Document Q&A with TinyLlama",
description="Ask questions about your documents"
)
# Hugging Face Spaces
iface.launch() |