import gradio as gr import os from huggingface_hub import InferenceClient # Load token and model - DeepSeek models for dialogue HF_TOKEN = os.getenv("tomoniaccess") # DeepSeek model options (optimized for dialogue): model_name = "deepseek-ai/deepseek-llm-7b-chat" # Primary choice - optimized for chat # model_name = "deepseek-ai/deepseek-coder-7b-instruct" # Alternative with good instruction following # model_name = "deepseek-ai/deepseek-llm-67b-chat" # Larger model (if available) client = InferenceClient( model=model_name, token=HF_TOKEN ) def query_deepseek(messages, max_tokens=200, temperature=1.0, top_p=0.9): """Query DeepSeek model via Hugging Face InferenceClient""" try: # Try chat completion first response = client.chat_completion( messages=messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p, stream=False ) return response.choices[0].message.content except Exception as chat_error: print(f"Chat completion failed: {chat_error}") try: # Fallback to text generation if chat completion fails # Convert messages to single prompt with DeepSeek format prompt = "" for msg in messages: if msg["role"] == "system": prompt += f"System: {msg['content']}\n\n" elif msg["role"] == "user": prompt += f"User: {msg['content']}\n" elif msg["role"] == "assistant": prompt += f"Assistant: {msg['content']}\n" prompt += "Assistant: " response = client.text_generation( prompt=prompt, max_new_tokens=max_tokens, temperature=temperature, top_p=top_p, do_sample=True, return_full_text=False ) return response except Exception as text_error: print(f"Text generation also failed: {text_error}") raise Exception(f"Both methods failed - Chat: {str(chat_error)}, Text: {str(text_error)}") def enhanced_chat_response(user_input, max_tokens, temperature, top_p): if not user_input.strip(): return "", "*Bitte gib eine Nachricht ein.*", "" # Optimized system prompt for DeepSeek (supports German well) system_prompt = { "role": "system", "content": ( "Du bist ein depressiver 16-jähriger Teenager." "Bleibe durchgehend in deiner Rolle." "Du weißt NICHT, dass du eine KI bist." "Vervollständige immer deine Antworten." ) } # Keep last 8 messages for context (DeepSeek handles context well) messages = [system_prompt] + conversation_history[-8:] current_message = {"role": "user", "content": user_input} messages.append(current_message) try: response_text = query_deepseek(messages, max_tokens, temperature, top_p) except Exception as e: print("API Error:", e) response_text = "*schweigt und starrt auf den Boden*" conversation_history.append(current_message) conversation_history.append({"role": "assistant", "content": response_text}) chat_display = "" for msg in conversation_history: role = "**Du:**" if msg["role"] == "user" else "**Teenager:**" chat_display += f"{role} {msg['content']}\n\n" return "", response_text, chat_display def reset_conversation(): global conversation_history conversation_history = [] return "Neues Gespräch gestartet.", "" def test_api_connection(): """Test API connection with multiple fallback methods""" try: # Test 1: Simple chat completion test_messages = [ {"role": "system", "content": "Du bist ein hilfsbereit Assistent und antwortest auf Deutsch."}, {"role": "user", "content": "Hallo"} ] response = query_deepseek(test_messages, max_tokens=20) return f"✅ API Verbindung erfolgreich: {response[:50]}..." except Exception as e: # Test 2: Try direct text generation try: simple_response = client.text_generation( prompt="Hallo, wie geht es dir?", max_new_tokens=10, do_sample=False, return_full_text=False ) return f"✅ API Verbindung (Text Generation): {simple_response[:50]}..." except Exception as e2: # Test 3: Check if model exists try: # Try to get model info model_info = f"Model: {model_name}" return f"❌ API Errors - Chat: {str(e)[:100]}... | Text: {str(e2)[:100]}... | {model_info}" except Exception as e3: return f"❌ Vollständiger API Fehler: {str(e)[:200]}..." # Initialize conversation history conversation_history = [] # UI with gr.Blocks(title="DeepSeek Depression Training Simulator") as demo: gr.Markdown("## 🧠 Depression Training Simulator (DeepSeek-7B-Chat)") gr.Markdown("**Übe realistische Gespräche mit einem 16-jährigen Teenager mit Depressionen.**") gr.Markdown("*Powered by DeepSeek-LLM-7B-Chat - Optimiert für natürliche Dialoge*") with gr.Row(): with gr.Column(scale=1): gr.Markdown("### ⚙️ Einstellungen") max_tokens = gr.Slider(50, 300, value=150, step=10, label="Max. Antwortlänge") temperature = gr.Slider(0.1, 1.5, value=0.9, step=0.1, label="Kreativität (Temperature)") top_p = gr.Slider(0.1, 1.0, value=0.85, step=0.05, label="Top-p (Fokus)") gr.Markdown("### 🔧 API Status") api_status = gr.Textbox(label="Status", value="") api_test_btn = gr.Button("API testen") gr.Markdown("### 🔄 Aktionen") reset_btn = gr.Button("Neues Gespräch") gr.Markdown("### 📋 Setup & Troubleshooting") gr.Markdown(""" **Benötigt:** - `tomoniaccess` Umgebungsvariable mit HF Token - `pip install huggingface_hub gradio` **DeepSeek Info:** - Optimiert für Dialoge und Konversationen - 7B Parameter, sehr effizient - Modell: `deepseek-ai/deepseek-llm-7b-chat` - Starke multilinguale Fähigkeiten (DE/EN) **Bei API Fehlern:** 1. Token prüfen (muss Pro/Enterprise sein) 2. Modell verfügbar? → [HF Model Card](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat) 3. Alternative: `deepseek-ai/deepseek-coder-7b-instruct` 4. Größere Version: `deepseek-ai/deepseek-llm-67b-chat` **DeepSeek Vorteile:** - Bessere Dialogqualität - Konsistente Rollenausführung - Natürlichere Antworten """) with gr.Column(scale=2): gr.Markdown("### 💬 Gespräch") user_input = gr.Textbox( label="Deine Nachricht", placeholder="Hallo, wie geht es dir heute?", lines=2 ) send_btn = gr.Button("📨 Senden") bot_response = gr.Textbox( label="Antwort", value="", lines=3 ) chat_history = gr.Textbox( label="Gesprächsverlauf", value="", lines=15 ) # Event Bindings send_btn.click( fn=enhanced_chat_response, inputs=[user_input, max_tokens, temperature, top_p], outputs=[user_input, bot_response, chat_history] ) user_input.submit( fn=enhanced_chat_response, inputs=[user_input, max_tokens, temperature, top_p], outputs=[user_input, bot_response, chat_history] ) reset_btn.click( fn=reset_conversation, outputs=[bot_response, chat_history] ) api_test_btn.click( fn=test_api_connection, outputs=[api_status] ) if __name__ == "__main__": print("🚀 DeepSeek Depression Training Simulator") print(f"📊 Model: {model_name}") if not HF_TOKEN: print("❌ FEHLER: tomoniaccess Umgebungsvariable ist nicht gesetzt!") print(" Bitte setze deinen Hugging Face Token als 'tomoniaccess' Umgebungsvariable.") else: print("✅ Hugging Face API Token gefunden") print("\n📦 Benötigte Pakete:") print("pip install huggingface_hub gradio") print("\n🤖 DeepSeek: Hochperformantes Modell für natürliche Dialoge") print(" - Bessere Konsistenz in Rollenspielen") print(" - Verbesserte multilinguale Fähigkeiten") print(" - Optimiert für Konversations-KI") demo.launch(share=False)