Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import os | |
| import requests | |
| # Load the Hugging Face token from environment variables (secrets) | |
| token = os.environ.get("KEY2") # Replace "KEY2" with your secret key name | |
| # Function to query the Hugging Face API | |
| def query_huggingface_api(prompt, max_new_tokens=50, temperature=0.7, top_k=50): | |
| model_name = "HuggingFaceH4/zephyr-7b-alpha" # Replace with your preferred model | |
| api_url = f"https://api-inference.huggingface.co/models/{model_name}" | |
| headers = {"Authorization": f"Bearer {token}"} | |
| payload = { | |
| "inputs": prompt, | |
| "parameters": { | |
| "max_new_tokens": max_new_tokens, | |
| "temperature": temperature, | |
| "top_k": top_k, | |
| }, | |
| } | |
| response = requests.post(api_url, headers=headers, json=payload) | |
| if response.status_code == 200: | |
| return response.json()[0]["generated_text"] | |
| else: | |
| st.error(f"Error: {response.status_code} - {response.text}") | |
| return None | |
| # Streamlit App | |
| def main(): | |
| st.title("Hugging Face API Test") | |
| st.write("Enter a prompt and get a response from the model.") | |
| # Input prompt | |
| prompt = st.text_input("Enter your prompt:") | |
| if prompt: | |
| st.write("**Prompt:**", prompt) | |
| # Query the Hugging Face API | |
| response = query_huggingface_api(prompt) | |
| if response: | |
| st.write("**Response:**", response) | |
| if __name__ == "__main__": | |
| main() |