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")