| import os | |
| from huggingface_hub import InferenceClient | |
| # We use Meta's Llama 3 model here, which is incredibly smart and completely FREE on Hugging Face! | |
| client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct") | |
| def decode_semantic_intent(corrupted_text: str) -> str: | |
| prompt = f"A message was destroyed by wireless noise. The demodulator outputted: '{corrupted_text}'. Reconstruct the original intent perfectly. Only output the corrected sentence and absolutely nothing else." | |
| messages = [ | |
| {"role": "system", "content": "You are a highly advanced 6G Semantic Communication Decoder. You output only the fixed sentence. No conversational text. No explanations."}, | |
| {"role": "user", "content": prompt} | |
| ] | |
| try: | |
| # Ask the free Hugging Face model to fix the text | |
| response = client.chat_completion( | |
| messages, | |
| max_tokens=100, | |
| temperature=0.1 | |
| ) | |
| return response.choices[0].message.content.strip() | |
| except Exception as e: | |
| return f"AI Decoding Failed: {str(e)}" |