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import os
import gradio as gr
from groq import Groq
def generate_response(prompt, history, model, temperature, max_tokens, top_p):
client = Groq(
api_key = os.environ.get("Groq_Api_Key")
)
stream = client.chat.completions.create(
messages=[
{"role": "system", "content": "you are a helpful assistant."},
{"role": "user", "content": prompt}
],
model=model,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
stop=None,
stream=True,
)
response = ""
for chunk in stream:
delta_content = chunk.choices[0].delta.content
if delta_content is not None:
response += delta_content
return response
# Define the Gradio chat interface
additional_inputs = [
gr.Dropdown(choices=["llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "llama2-70b-4096", "gemma-7b-it"], value="llama3-70b-8192", label="LLM Model"),
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Temperature"),
gr.Slider(minimum=1, maximum=4096, step=1, value=4096, label="Max Tokens"),
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Top P"),
]
gr.ChatInterface(
fn=generate_response,
chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
additional_inputs=additional_inputs,
title="Groq API LLMs AI Models",
description="Using https://groq.com/ api, ofc as its free it will have some limitations so its better if you duplicate this space with your own api key<br>Hugging Face Space by [Nick088](https://linktr.ee/Nick088)",
).launch() |