app.py

import os
import streamlit as st
from groq import Groq

load_dotnev()

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: {e}")
    return None  # Indicate failur

api1 = os.getenv("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.")
    # 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")