| import streamlit as st | |
| import os | |
| from huggingface_hub import InferenceClient | |
| # Get token from Space secrets | |
| API_TOKEN = os.getenv("HF_TOKEN") | |
| # Initialize the Inference Client | |
| client = InferenceClient(token=API_TOKEN) | |
| # Title of the app | |
| st.title("Sentence Improver App") | |
| # Text input from the user | |
| user_input = st.text_input("Enter a sentence to improve:", "I goed to the park and play.") | |
| # Button to trigger the correction | |
| if st.button("Improve Sentence"): | |
| if user_input: | |
| # Create a prompt for the LLM to improve the sentence | |
| prompt = f"Correct and improve this sentence: '{user_input}'" | |
| # Call the LLM via Hugging Face Inference API | |
| try: | |
| response = client.text_generation( | |
| prompt, | |
| model="mistralai/Mixtral-8x7B-Instruct-v0.1", # You can change this to another model | |
| #model="deepseek-ai/deepseek-coder-6.7b-instruct", | |
| max_new_tokens=100, | |
| temperature=0.7, | |
| ) | |
| # Extract the improved sentence (remove the prompt part if included) | |
| improved_sentence = response.strip() | |
| st.write("Improved Sentence:", improved_sentence) | |
| except Exception as e: | |
| st.error(f"Error calling the LLM: {str(e)}") | |
| else: | |
| st.warning("Please enter a sentence first!") | |
| else: | |
| st.write("Enter a sentence and click the button to see it improved!") | |