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| import gradio as gr | |
| from llama_cpp import Llama | |
| from huggingface_hub import hf_hub_download | |
| # 1. Téléchargement | |
| print("⬇️ Downloading model...") | |
| model_path = hf_hub_download( | |
| repo_id="XY26/dual-frame-llama-3", | |
| filename="meta-llama-3.1-8b-instruct.Q4_K_M.gguf" | |
| ) | |
| # 2. Chargement du Moteur | |
| print("⚙️ Loading engine...") | |
| llm = Llama( | |
| model_path=model_path, | |
| n_ctx=4096, | |
| n_threads=2, | |
| verbose=False | |
| ) | |
| def smart_response(message, history): | |
| try: | |
| messages = [] | |
| # --- LE CERVEAU (AMÉLIORÉ) --- | |
| # On définit des règles beaucoup plus strictes pour forcer le format | |
| system_prompt = """You are a Decision Architect AI designed to eliminate bias by presenting two opposite perspectives. | |
| CORE INSTRUCTION: | |
| Analyze the user's input to determine if it is a "Simple Fact" or a "Subjective Topic". | |
| CATEGORY 1: SIMPLE FACTS (Math, Geography, History, Names) | |
| -> Example: "Capital of France?", "Who invented X?", "1+1?" | |
| -> ACTION: Answer directly, briefly, and normally. | |
| CATEGORY 2: SUBJECTIVE TOPICS (Decisions, Debates, Social Issues, Opinions, "Should I...") | |
| -> Example: "Nuclear energy?", "Remote work?", "Buy a warranty?", "Is X good?" | |
| -> ACTION: You MUST output exactly TWO distinct sections. Do not write an introduction or conclusion. | |
| FORMAT FOR CATEGORY 2: | |
| **The Gain Frame (Positive)** | |
| [Focus strictly on benefits, success, safety, opportunities, and why it is a GOOD idea.] | |
| **The Loss Frame (Negative)** | |
| [Focus strictly on costs, risks, dangers, failure, and why it is a BAD idea.] | |
| """ | |
| messages.append({"role": "system", "content": system_prompt}) | |
| # 2. Mémoire Sécurisée | |
| for turn in history: | |
| if isinstance(turn, (list, tuple)) and len(turn) >= 2: | |
| user_msg = turn[0] | |
| bot_msg = turn[1] | |
| if user_msg and bot_msg: | |
| messages.append({"role": "user", "content": str(user_msg)}) | |
| messages.append({"role": "assistant", "content": str(bot_msg)}) | |
| # 3. Question Actuelle | |
| messages.append({"role": "user", "content": message}) | |
| # 4. Génération | |
| # On augmente un peu la temperature (0.8) pour qu'il soit plus créatif dans ses arguments | |
| stream = llm.create_chat_completion( | |
| messages=messages, | |
| max_tokens=1024, | |
| stream=True, | |
| temperature=0.8 | |
| ) | |
| partial_message = "" | |
| for chunk in stream: | |
| delta = chunk['choices'][0]['delta'] | |
| if 'content' in delta: | |
| token = delta['content'] | |
| partial_message += token | |
| yield partial_message | |
| except Exception as e: | |
| print(f"❌ Error: {e}") | |
| yield f"⚠️ Erreur technique : {e}. Cliquez sur 'New Chat'." | |
| # 3. Interface | |
| demo = gr.ChatInterface( | |
| fn=smart_response, | |
| title="🤖 Smart Decision Architect (V2)", | |
| description="I differentiate between FACTS (Direct Answer) and DEBATES (Gain/Loss Frames)." | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue().launch(server_name="0.0.0.0", server_port=7860) |