from huggingface_hub import hf_hub_download

n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool.
n_batch = 256 

import paperscraper
from paperqa import Docs
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.embeddings import LlamaCppEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from g4f import Provider, models
from langchain.llms.base import LLM

from langchain_g4f import G4FLLM

# Make sure the model path is correct for your system!
llm = LLM = G4FLLM(
        model=models.gpt_35_turbo,
        provider=Provider.Aichat,
    )
    
from langchain.embeddings import HuggingFaceEmbeddings

model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
embeddings = HuggingFaceEmbeddings(
    model_name=model_name,
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs
)
docs = Docs(llm=llm, embeddings=embeddings)

keyword_search = 'bispecific antibody manufacture'
papers = paperscraper.search_papers(keyword_search, limit=2)
for path,data in papers.items():
    try:
        docs.add(path,chunk_chars=500)
    except ValueError as e:
        print('Could not read', path, e)

answer = docs.query("What manufacturing challenges are unique to bispecific antibodies?")
print(answer)

def re(r):

    print(answer)
    return r 

gr.Interface(fn=re,inputs=gr.Textbox(),outputs=gr.Textbox).launch()