Spaces:
Running
Running
File size: 1,507 Bytes
2694503 f2fa1a5 2694503 b0e4f97 2694503 b2801d6 2694503 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
import os
import streamlit as st
from groq import Groq
client = Groq(
api_key=os.environ.get("GROQ_API_KEY"),
)
query = st.text_input("Enter your query")
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": query,
}
],
model="mixtral-8x7b-32768",
)
st.write(chat_completion.choices[0].message.content)
print(chat_completion)
# # Text to 3D
# import streamlit as st
# import torch
# from diffusers import ShapEPipeline
# from diffusers.utils import export_to_gif
# # Model loading (Ideally done once at the start for efficiency)
# ckpt_id = "openai/shap-e"
# @st.cache_resource # Caches the model for faster subsequent runs
# def load_model():
# return ShapEPipeline.from_pretrained(ckpt_id).to("cuda")
# pipe = load_model()
# # App Title
# st.title("Shark 3D Image Generator")
# # User Inputs
# prompt = st.text_input("Enter your prompt:", "a shark")
# guidance_scale = st.slider("Guidance Scale", 0.0, 20.0, 15.0, step=0.5)
# # Generate and Display Images
# if st.button("Generate"):
# with st.spinner("Generating images..."):
# images = pipe(
# prompt,
# guidance_scale=guidance_scale,
# num_inference_steps=64,
# size=256,
# ).images
# gif_path = export_to_gif(images, "shark_3d.gif")
# st.image(images[0]) # Display the first image
# st.success("GIF saved as shark_3d.gif") |