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import os
import gradio as gr
from groq import Groq
from dotenv import load_dotenv
import json
from deep_translator import GoogleTranslator
load_dotenv()
api1 = os.getenv("GROQ_API_KEY")
api2 = os.getenv("Groq_key")
api3 = os.getenv("GRoq_key")
# api2 = os.getenv("Groq_key")
# api2 = os.getenv("Groq_key")
# api2 = os.getenv("Groq_key")
# api2 = os.getenv("Groq_key")
apis = [
api1,
api2,
api3,
]
def make_call(data):
print(data)
newdata = data.replace("'", '"')
items = json.loads(newdata)
language = items['lang']
query = items['text']
query = query.lower()
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"Answer this query in a short message with wisdom, love and compassion, in context to bhagwat geeta, that feels like chatting to a person and provide references of shloks from chapters of bhagwat geeta which is relevant to the query. keep the answer short, precise and simple. Query= {query}"
try:
response = client.chat.completions.create(
messages=[
{
"role": "user",
"content": prmptquery,
}
],
model="mixtral-8x7b-32768",
)
answer = response.choices[0].message.content
translated = GoogleTranslator(source='auto', target=language).translate(answer)
except Exception as e:
print(f"API call failed for: {e}")
if answer:
break
if answer:
break
respo = {
"message": translated,
"action": "nothing",
"function": "nothing",
}
print(translated)
return json.dumps(respo)
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") |