app.py

from langchain import PromptTemplate, LLMChain
from langchain.llms import CTransformers
import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceBgeEmbeddings
from io import BytesIO
from langchain.document_loaders import PyPDFLoader
import gradio as gr


local_llm = "final_model_maybe_gguf-unsloth.Q5_K_M.gguf"

config = {
'max_new_tokens': 2048,
'repetition_penalty': 1.1,
'temperature': 0.6,
'top_k': 50,
'top_p': 0.9,
'stream': True,
'threads': int(os.cpu_count() / 2)
}

llm = CTransformers(
    model=local_llm,
    model_type="mistral",
    lib="avx2", #for CPU use
    **config
)

print("LLM Initialized...")


prompt_template = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
제시된 질문에 대해서 context 내용으로 답변해줘.

### Context :
{context}

### Instruction:
{question}

### Response:
"""

model_name = "jhgan/ko-sroberta-multitask"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
embeddings = HuggingFaceBgeEmbeddings(
    model_name=model_name,
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs
)


prompt = PromptTemplate(template=prompt_template, input_variables=['context', 'question'])
load_vector_store = Chroma(persist_directory="stores/pet_cosine", embedding_function=embeddings)
retriever = load_vector_store.as_retriever(search_kwargs={"k":1})
# query = "what is the fastest speed for a greyhound dog?"
# semantic_search = retriever.get_relevant_documents(query)
# print(semantic_search)

print("######################################################################")

chain_type_kwargs = {"prompt": prompt}

# qa = RetrievalQA.from_chain_type(
#     llm=llm,
#     chain_type="stuff",
#     retriever=retriever,
#     return_source_documents = True,
#     chain_type_kwargs= chain_type_kwargs,
#     verbose=True
# )

# response = qa(query)

# print(response)

sample_prompts = ["what is the fastest speed for a greyhound dog?", "Why should we not feed chocolates to the dogs?", "Name two factors which might contribute to why some dogs might get scared?"]

def get_response(input):
  query = input
  chain_type_kwargs = {"prompt": prompt}
  qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True, chain_type_kwargs=chain_type_kwargs, verbose=True)
  response = qa(query)
  return response

input = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

iface = gr.Interface(fn=get_response, 
             inputs=input, 
             outputs="text",
             title="My Dog PetCare Bot",
             description="This is a RAG implementation based on Zephyr 7B Beta LLM.",
             examples=sample_prompts,
             allow_screenshot=False,
             allow_flagging=False
             )

iface.launch()