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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 short with wisdom, love and compassion, in context to bhagwat geeta, 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") |