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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import os | |
| # Get HF token from environment variable | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| # Initialize client with proper error handling | |
| def get_client(): | |
| if HF_TOKEN: | |
| try: | |
| # Try with the preferred model first | |
| return InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=HF_TOKEN) | |
| except Exception as e: | |
| print(f"Failed to initialize zephyr model: {e}") | |
| # Fallback to mistral with token | |
| try: | |
| return InferenceClient("mistralai/Mistral-7B-Instruct-v0.1", token=HF_TOKEN) | |
| except Exception as e2: | |
| print(f"Failed to initialize mistral model: {e2}") | |
| return None | |
| else: | |
| print("No HF_TOKEN found. Please set your Hugging Face token.") | |
| return None | |
| client = get_client() | |
| # Dynamic prompt builder based on CEFR level | |
| def level_to_prompt(level): | |
| return { | |
| "A1": "You are a friendly French tutor. Speak mostly in English, use simple French, and explain everything.", | |
| "A2": "You are a patient French tutor. Use short French phrases and explain them in English.", | |
| "B1": "You are a helpful French tutor. Speak mostly in French but clarify in English when needed.", | |
| "B2": "You are a French tutor. Speak primarily in French with rare English support.", | |
| "C1": "You are a native French tutor. Speak entirely in French, clearly and professionally.", | |
| "C2": "You are a native French professor. Speak in rich, complex French. Avoid English." | |
| }.get(level, "You are a helpful French tutor.") | |
| # Custom background CSS | |
| css = """ | |
| @import url('https://fonts.googleapis.com/css2?family=Noto+Sans+JP&family=Playfair+Display&display=swap'); | |
| body { | |
| background-image: url('https://cdn-uploads.huggingface.co/production/uploads/67351c643fe51cb1aa28f2e5/wuyd5UYTh9jPrMJGmV9yC.jpeg'); | |
| background-size: cover; | |
| background-position: center; | |
| background-repeat: no-repeat; | |
| } | |
| .gradio-container { | |
| display: flex; | |
| flex-direction: column; | |
| justify-content: center; | |
| min-height: 100vh; | |
| padding-top: 2rem; | |
| padding-bottom: 2rem; | |
| } | |
| #chat-panel { | |
| background-color: rgba(255, 255, 255, 0.85); | |
| padding: 2rem; | |
| border-radius: 12px; | |
| max-width: 700px; | |
| height: 70vh; | |
| margin: auto; | |
| box-shadow: 0 0 12px rgba(0, 0, 0, 0.3); | |
| overflow-y: auto; | |
| } | |
| .gradio-container .chatbot h1 { | |
| color: var(--custom-title-color) !important; | |
| font-family: 'Playfair Display', serif !important; | |
| font-size: 5rem !important; | |
| font-weight: bold !important; | |
| text-align: center !important; | |
| margin-bottom: 1.5rem !important; | |
| width: 100%; | |
| } | |
| """ | |
| # Chat logic with proper error handling | |
| def respond(message, history, level, max_tokens, temperature, top_p): | |
| # Check if client is available | |
| if client is None: | |
| yield "❌ Désolé! The AI service is not available. Please check your Hugging Face token configuration." | |
| return | |
| system_message = level_to_prompt(level) | |
| # Generate response | |
| response = "" | |
| try: | |
| # Create a proper prompt format for instruction-following models | |
| prompt = f"<|system|>\n{system_message}\n\n" | |
| # Add conversation history | |
| if history: | |
| for turn in history: | |
| if isinstance(turn, dict): | |
| if turn.get("role") == "user": | |
| prompt += f"<|user|>\n{turn['content']}\n\n" | |
| elif turn.get("role") == "assistant": | |
| prompt += f"<|assistant|>\n{turn['content']}\n\n" | |
| else: | |
| # Handle tuple format (user, assistant) | |
| user_msg, bot_msg = turn | |
| if user_msg: | |
| prompt += f"<|user|>\n{user_msg}\n\n" | |
| if bot_msg: | |
| prompt += f"<|assistant|>\n{bot_msg}\n\n" | |
| # Add current user message | |
| prompt += f"<|user|>\n{message}\n\n<|assistant|>\n" | |
| # Generate response with streaming | |
| for token in client.text_generation( | |
| prompt, | |
| max_new_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| do_sample=True, | |
| return_full_text=False, | |
| stop_sequences=["<|user|>", "<|system|>"] # Stop if model tries to continue conversation | |
| ): | |
| if token: # Handle None tokens | |
| # Clean up any unwanted tokens | |
| token = token.replace("<|user|>", "").replace("<|system|>", "").replace("<|assistant|>", "") | |
| if token.strip(): # Only add non-empty tokens | |
| response += token | |
| yield response | |
| except Exception as e: | |
| error_msg = str(e) | |
| print(f"Error in chat completion: {e}") | |
| if "401" in error_msg or "Unauthorized" in error_msg: | |
| yield "🔑 Authentication Error: Please check your Hugging Face token. Make sure it's valid and has the correct permissions." | |
| elif "429" in error_msg or "rate limit" in error_msg.lower(): | |
| yield "⏰ Rate limit exceeded. Please wait a moment before trying again." | |
| elif "503" in error_msg or "Service Unavailable" in error_msg: | |
| yield "🔧 The AI service is temporarily unavailable. Please try again later." | |
| else: | |
| yield f"❌ Désolé! There was an error: {error_msg}" | |
| # UI layout | |
| with gr.Blocks(css=css, title="French Tutor") as demo: | |
| gr.Markdown("# 🇫🇷 French Tutor", elem_id="custom-title") | |
| # Add status indicator | |
| if client is None: | |
| gr.Markdown("⚠️ **Warning**: No Hugging Face token found. Please set your HF_TOKEN environment variable.") | |
| else: | |
| gr.Markdown("✅ **Status**: Connected to AI service") | |
| with gr.Column(elem_id="chat-panel"): | |
| gr.Markdown(""" | |
| with gr.Accordion("⚙️ Advanced Settings", open=False): | |
| level = gr.Dropdown( | |
| choices=["A1", "A2", "B1", "B2", "C1", "C2"], | |
| value="A1", | |
| label="Your French Level (CEFR)", | |
| info="Choose your current French proficiency level" | |
| ) | |
| max_tokens = gr.Slider( | |
| 1, 2048, | |
| value=512, | |
| step=1, | |
| label="Response Length", | |
| info="Maximum number of tokens in the response" | |
| ) | |
| temperature = gr.Slider( | |
| 0.1, 2.0, | |
| value=0.7, | |
| step=0.1, | |
| label="Creativity", | |
| info="Higher values make responses more creative" | |
| ) | |
| top_p = gr.Slider( | |
| 0.1, 1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Dynamic Text", | |
| info="Controls text diversity" | |
| ) | |
| gr.ChatInterface( | |
| fn=respond, | |
| additional_inputs=[level, max_tokens, temperature, top_p], | |
| type="messages", | |
| title="Chat with your French Tutor", | |
| description="Ask questions, practice conversation, or get help with French grammar!" | |
| ) | |
| """) | |
| if __name__ == "__main__": | |
| demo.launch() |