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from huggingface_hub import InferenceClient, login
from transformers import AutoTokenizer
from langchain.chat_models import ChatOpenAI
import os, sys, json
import gradio as gr
from langchain.evaluation import load_evaluator
from pprint import pprint as print
# access token with permission to access the model and PRO subscription
#HUGGINGFACEHUB_API_TOKEN = os.getenv("HF_ACCESS_READ")
OAI_API_KEY=os.getenv("OPENAI_API_KEY")
login(token=os.environ["HF_ACCESS_READ"])
# tokenizer for generating prompt
print ("Tokenizer")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-70b-chat-hf")
# inference client
print ("Inf.Client")
client = InferenceClient("https://api-inference.huggingface.co/models/meta-llama/Llama-2-70b-chat-hf")
# generate function
def generate(text, history):
payload = tokenizer.apply_chat_template([{"role":"user","content":text}],tokenize=False)
res = client.text_generation(
payload,
do_sample=True,
return_full_text=False,
max_new_tokens=2048,
top_p=0.9,
temperature=0.6,
)
#zum Evaluieren:
# custom eli5 criteria
custom_criterion = {"eli5": "Is the output explained in a way that a 5 yeard old would unterstand it?"}
eval_result = evaluator.evaluate_strings(prediction=res.strip(), input=text, criteria=custom_criterion, requires_reference=True)
print ("eval_result:............ ")
print(eval_result)
return res.strip()
########################################
#Evaluation
########################################
evaluation_llm = ChatOpenAI(model="gpt-4")
# create evaluator
evaluator = load_evaluator("criteria", criteria="conciseness", llm=evaluation_llm)
################################################
#GUI
###############################################
#Beschreibung oben in GUI
################################################
chatbot_stream = gr.Chatbot()
chat_interface_stream = gr.ChatInterface(fn=generate,
title = "ChatGPT vom LI",
theme="soft",
chatbot=chatbot_stream,
retry_btn="🔄 Wiederholen",
undo_btn="↩️ Letztes löschen",
clear_btn="🗑️ Verlauf löschen",
submit_btn = "Abschicken",
)
with gr.Blocks() as demo:
with gr.Tab("Chatbot"):
#chatbot_stream.like(vote, None, None)
chat_interface_stream.queue().launch()
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