import os
import gradio as gr
from groq import Groq
from dotenv import load_dotenv
load_dotenv()

api1 = os.getenv("GROQ_API_KEY")

apis = [
    api1,
    # api1,
]


def make_call(data):
    print(data)
    answer = None
    while True:
        for api in apis:
            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= {data}"
            try:
                response = client.chat.completions.create(
                messages=[
                    {
                        "role": "user",
                        "content": prmptquery,
                    }
                ],
                model="mixtral-8x7b-32768",
                )
                answer = response.choices[0].message.content
            except Exception as e:
                print(f"API call failed for: {e}")
            if answer:
                break
        if answer:
                break
    return answer



gradio_interface = gr.Interface(fn=make_call, inputs="text", outputs="text")
gradio_interface.launch()

# 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")