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")