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import gradio as gr | |
from openai import OpenAI | |
import os | |
css = ''' | |
.gradio-container{max-width: 1000px !important} | |
h1{text-align:center} | |
footer { | |
visibility: hidden | |
} | |
''' | |
ACCESS_TOKEN = os.getenv("HF_TOKEN") | |
client = OpenAI( | |
base_url="https://api-inference.huggingface.co/v1/", | |
api_key=ACCESS_TOKEN, | |
) | |
def respond( | |
message, | |
history, | |
max_tokens, | |
temperature, | |
system_message=""" Extract the following information from the given text: | |
Identify the specific areas where the work needs to be done and Add the furniture that has to be changed. | |
Do not specify the work that has to be done. | |
Format the extracted information in the following JSON structure: | |
{ | |
"Area Type1": { | |
"Furniture1", | |
"Furniture2", | |
... | |
} | |
"Area Type2": { | |
"Furniture1", | |
"Furniture2", | |
... | |
} | |
}""", | |
): | |
messages = [{"role": "system", "content": system_message}] | |
if len(history) == 0: | |
pass | |
else: | |
history.pop() | |
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.chat.completions.create( | |
model="meta-llama/Meta-Llama-3.1-8B-Instruct", | |
max_tokens=max_tokens, | |
stream=True, | |
temperature=temperature, | |
messages=messages, | |
): | |
token = message.choices[0].delta.content | |
response += token | |
yield response | |
demo = gr.ChatInterface( | |
respond, | |
additional_inputs=[ | |
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="Temperature"), | |
], | |
css=css, | |
theme="allenai/gradio-theme", | |
) | |
if __name__ == "__main__": | |
demo.launch(debug = True) |