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import os
import streamlit as st
from groq import Groq
def make_call(api):
"""Calls the Groq API (assuming API key auth) and handles potential errors."""
try:
client = Groq(
api_key=api,
) # Configure the model with the API key
query = st.text_input("Enter your query")
prmptquery= f"give the answer of given query in context to bhagwat geeta: {query}"
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": prmptquery,
}
],
model="mixtral-8x7b-32768",
)
# print(response.text) # Return the response for further processing
return chat_completion.choices[0].message.content
except Exception as e:
print(f"API call failed for key {api_key}: {e}")
return None # Indicate failur
api1 = os.environ.get("GROQ_API_KEY"),
# api1 = os.environ.get("GROQ_API_KEY"),
# api1 = os.environ.get("GROQ_API_KEY"),
# api1 = os.environ.get("GROQ_API_KEY"),
# api1 = os.environ.get("GROQ_API_KEY"),
# api1 = os.environ.get("GROQ_API_KEY"),
apis = [
api1,
# api1,
]
# Loop indefinitely
data = None
# while True: # Loop indefinitely
for api in apis:
data = make_call(api)
if data: # Check for a successful response
st.write(chat_completion.choices[0].message.content)
break # Exit both the for loop and while loop
else:
st.write(f"Failed to retrieve data from key {api_key}.")
# if data: # If a successful response was found, break the outer while loop
# break
# 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") |