app.py

import os
import streamlit as st
from groq import Groq
from dotenv import load_dotenv
load_dotenv()

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"Act as bhagwan Krishna and answer this query in context to bhagwat geeta, you may also provide reference to shloks from chapters of bhagwat geeta which is relevant to the query. Query= {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(data)
        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")