| import streamlit as st | |
| import requests | |
| # # Print out all secrets to check what is available | |
| # st.write(st.secrets) | |
| # # Access the secret with the key "CTP_DATASCIENCE" (if that's how you've stored it) | |
| CTP_DATASCIENCE = st.secrets.get("CTP_DATASCIENCE") | |
| # # Check if the API key is available | |
| # if CTP_DATASCIENCE: | |
| # st.success("API key found!") | |
| # else: | |
| # st.error("API key not found!") | |
| # Set up the headers for the Hugging Face API request using the API key | |
| headers = {"Authorization": f"Bearer {CTP_DATASCIENCE}"} | |
| # Define the Hugging Face API URL (for Whisper model, in this case) | |
| API_URL = "https://api-inference.huggingface.co/models/openai/whisper-large-v3-turbo" | |
| # Function to make the API request with the given file | |
| def query(filename): | |
| with open(filename, "rb") as f: | |
| data = f.read() | |
| response = requests.post(API_URL, headers=headers, data=data) | |
| return response.json() | |
| # Example usage with a sample audio file | |
| output = query("sample1.flac") | |
| # Display the output of the API request | |
| st.write(output) | |