app.py

import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from sentence_transformers import SentenceTransformer
from datasets import load_dataset
import faiss
import gradio as gr
from accelerate import Accelerator

hf_api_key = os.getenv('HF_API_KEY')
model_id = "microsoft/phi-2"
tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_api_key, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    token=hf_api_key,
    trust_remote_code=True,
    torch_dtype=torch.float32
)

accelerator = Accelerator()
model = accelerator.prepare(model)

ST = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
dataset = load_dataset("not-lain/wikipedia", revision="embedded")
data = dataset["train"]
data = data.add_faiss_index("embeddings")

def search(query: str, k: int = 3):
    embedded_query = ST.encode(query)
    scores, retrieved_examples = data.get_nearest_examples("embeddings", embedded_query, k=k)
    return scores, retrieved_examples

def format_prompt(prompt, retrieved_documents, k):
    PROMPT = f"Question:{prompt}\nContext:"
    for idx in range(k):
        PROMPT += f"{retrieved_documents['text'][idx]}\n"
    return PROMPT

def rag_chatbot_interface(prompt: str, k: int = 2):
    scores, retrieved_documents = search(prompt, k)
    formatted_prompt = format_prompt(prompt, retrieved_documents, k)
    return generate(formatted_prompt)

SYS_PROMPT = "You are an assistant for answering questions. Provide a conversational answer."

iface = gr.Interface(
    fn=rag_chatbot_interface,
    inputs="text",
    outputs="text",
    title="Retrieval-Augmented Generation Chatbot",
    description="This chatbot provides more accurate answers by searching relevant documents and generating responses."
)

iface.launch(share=True)