app.py

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext


documents = SimpleDirectoryReader("Data").load_data()

import torch

from llama_index.llms import LlamaCPP
from llama_index.llms.llama_utils import messages_to_prompt, completion_to_prompt
llm = LlamaCPP(
    # You can pass in the URL to a GGML model to download it automatically
    model_url='https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf',
    # optionally, you can set the path to a pre-downloaded model instead of model_url
    model_path=None,
    temperature=0.1,
    max_new_tokens=256,
    # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
    context_window=3900,
    # kwargs to pass to __call__()
    generate_kwargs={},
    # kwargs to pass to __init__()
    # set to at least 1 to use GPU
    model_kwargs={"n_gpu_layers": -1},
    # transform inputs into Llama2 format
    messages_to_prompt=messages_to_prompt,
    completion_to_prompt=completion_to_prompt,
    verbose=True,
)




from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index.embeddings import LangchainEmbedding
from llama_index import ServiceContext

embed_model = LangchainEmbedding(
  HuggingFaceEmbeddings(model_name="thenlper/gte-large")
)


service_context = ServiceContext.from_defaults(
    chunk_size=256,
    llm=llm,
    embed_model=embed_model
)

index = VectorStoreIndex.from_documents(documents, service_context=service_context)

query_engine = index.as_query_engine()
#response = query_engine.query("What is Fibromyalgia?")

import gradio as gr

def text_to_uppercase(text):
    response=query_engine.query(text)
    return response

iface = gr.Interface(fn=text_to_uppercase, inputs="text", outputs="text")
iface.launch()