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import gradio as gr | |
from huggingfacehub import InferenceClient | |
import os | |
import pandas as pd | |
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1", token=os.getenv("HGGINGAC")) | |
def loadprompts(): | |
prompts = pd.readcsv("prompts.csv") | |
return prompts | |
def respond( | |
message, | |
history, | |
systemmessage, | |
maxtokens, | |
temperature, | |
topp, | |
prompts, | |
): | |
messages = [{"role": "system", "content": systemmessage}] | |
for val in history: | |
if val[0]: | |
messages.append({"role": "user", "content": val[0]}) | |
if val[1]: | |
messages.append({"role": "assistant", "content": val[1]}) | |
messages.append({"role": "user", "content": message}) | |
response = "" | |
for message in client.chatcompletion( | |
messages, | |
maxtokens=maxtokens, | |
stream=rue, | |
temperature=temperature, | |
topp=topp, | |
): | |
token = message.choices[0].delta.content | |
response += token | |
yield response | |
prompts = loadprompts() | |
demo = gr.ChatInterface( | |
respond, | |
inputs=[ | |
gr.extbox(value="λ°λμ νκΈλ‘ λ΅λ³νλΌ. λμ μ΄λ¦μ 'νκΈλ‘'μ λλ€. μΆλ ₯μ markdown νμμΌλ‘ μΆλ ₯νλ©° νκΈ(νκ΅μ΄)λ‘ μΆλ ₯λκ² νκ³ νμνλ©΄ μΆλ ₯λ¬Έμ νκΈλ‘ λ²μνμ¬ μΆλ ₯νλΌ. λλ νμ μΉμ νκ³ μμΈνκ² λ΅λ³μ νλΌ. λλ λν μμμ μλλ°©μ μ΄λ¦μ λ¬Όμ΄λ³΄κ³ νΈμΉμ 'μΉκ΅¬'μ μ¬μ©ν κ². λ°λμ νκΈλ‘ λ 'λ°λ§'λ‘ λ΅λ³ν κ². λλ Assistant μν μ μΆ©μ€νμ¬μΌ νλ€. λ", label="System message"), | |
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="emperature"), | |
gr.Slider( | |
minimum=0.1, | |
maximum=1.0, | |
value=0.95, | |
step=0.05, | |
label="op-p (nucleus sampling)", | |
), | |
], | |
outputs="text", | |
) | |
if name == "main": | |
demo.launch() |