app.py

from fastapi import FastAPI
from llama_cpp import Llama
import streamlit as st

llm = Llama(
    model_path="Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
)
prompt = st.chat_input("Say something")
if prompt:
    st.write(f"User has sent the following prompt: {prompt}")

## create a new FASTAPI app instance
# app=FastAPI()

# Initialize the text generation pipeline
#pipe = pipeline("text2text-generation", model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF",token=os.getenv('HF_KEY'))


# @app.get("/")
# def home():
#     print("helloe here")
#     output= llm("What is the difference btw RAG and Fine tunning", max_tokens=1000)
#     print(output["choices"][0]["text"])
#     ## return the generate text in Json reposne
#     return {"output":output["choices"][0]["text"]}

# # Define a function to handle the GET request at `/generate`


# @app.get("/generate")
# def generate(text:str):
#     ## use the pipeline to generate text from given input text
#     print("Recieved prompt "+str(text))
#     output= llm(text, max_tokens=1000)
#     print(output["choices"][0]["text"])
#     ## return the generate text in Json reposne
#     return {"output":output["choices"][0]["text"]}