Spaces:
Paused
Paused
| import subprocess | |
| import os | |
| import gradio as gr | |
| from groq import Groq | |
| groq_api_key = os.environ('Groq_Api_key') | |
| subprocess.run(["export", f"GROQ_API_KEY={groq_api_key}"], check=True) | |
| def generate_response(input_text): | |
| client = Groq() | |
| stream = client.chat.completions.create( | |
| messages=[ | |
| {"role": "system", "content": "you are a helpful assistant."}, | |
| {"role": "user", "content": input_text} | |
| ], | |
| model="mixtral-8x7b-32768", | |
| temperature=0.5, | |
| max_tokens=1024, | |
| top_p=1, | |
| stop=None, | |
| stream=True, | |
| ) | |
| response = "" | |
| for chunk in stream: | |
| response += chunk.choices[0].delta.content | |
| return response | |
| # Define the Gradio UI | |
| inputs = gr.Textbox(label="Enter your question") | |
| outputs = gr.Textbox(label="Model Response") | |
| gr.Interface( | |
| fn=generate_response, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title="Language Model Assistant", | |
| description="Ask questions and get responses from a language model.", | |
| ).launch(show_api=False, share=True) |