import gradio as gr from huggingface_hub import InferenceClient # Initialize clients text_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") image_client = InferenceClient("SG161222/RealVisXL_V3.0") def check_custom_responses(message: str) -> str: """Check for specific patterns and return custom responses.""" message_lower = message.lower() custom_responses = { "what is ur name?": "xylaria", "what is ur Name?": "xylaria", "what is Ur name?": "xylaria", "what is Ur Name?": "xylaria", "What is ur name?": "xylaria", "What is ur Name?": "xylaria", "What is Ur name?": "xylaria", "What is Ur Name?": "xylaria", "what's ur name?": "xylaria", "what's ur Name?": "xylaria", "what's Ur name?": "xylaria", "what's Ur Name?": "xylaria", "whats ur name?": "xylaria", "whats ur Name?": "xylaria", "whats Ur name?": "xylaria", "whats Ur Name?": "xylaria", "what's your name?": "xylaria", "what's your Name?": "xylaria", "what's Your name?": "xylaria", "what's Your Name?": "xylaria", "Whats ur name?": "xylaria", "Whats ur Name?": "xylaria", "Whats Ur name?": "xylaria", "Whats Ur Name?": "xylaria", "What Is Your Name?": "xylaria", "What Is Ur Name?": "xylaria", "What Is Your Name?": "xylaria", "What Is Ur Name?": "xylaria", "what is your name?": "xylaria", "what is your Name?": "xylaria", "what is Your name?": "xylaria", "what is Your Name?": "xylaria", "how many 'r' is in strawberry?": "3", "how many 'R' is in strawberry?": "3", "how many 'r' Is in strawberry?": "3", "how many 'R' Is in strawberry?": "3", "How many 'r' is in strawberry?": "3", "How many 'R' is in strawberry?": "3", "How Many 'r' Is In Strawberry?": "3", "How Many 'R' Is In Strawberry?": "3", "how many r is in strawberry?": "3", "how many R is in strawberry?": "3", "how many r Is in strawberry?": "3", "how many R Is in strawberry?": "3", "How many r is in strawberry?": "3", "How many R is in strawberry?": "3", "How Many R Is In Strawberry?": "3", "how many 'r' in strawberry?": "3", "how many r's are in strawberry?": "3", "how many Rs are in strawberry?": "3", "How Many R's Are In Strawberry?": "3", "How Many Rs Are In Strawberry?": "3", "who is your developer?": "sk md saad amin", "who is your Developer?": "sk md saad amin", "who is Your Developer?": "sk md saad amin", "who is ur developer?": "sk md saad amin", "who is ur Developer?": "sk md saad amin", "who is Your Developer?": "sk md saad amin", "Who is ur developer?": "sk md saad amin", "Who is ur Developer?": "sk md saad amin", "who is ur dev?": "sk md saad amin", "Who is ur dev?": "sk md saad amin", "who is your dev?": "sk md saad amin", "Who is your dev?": "sk md saad amin", "Who's your developer?": "sk md saad amin", "Who's ur developer?": "sk md saad amin", "Who Is Your Developer?": "sk md saad amin", "Who Is Ur Developer?": "sk md saad amin", "Who Is Your Dev?": "sk md saad amin", "Who Is Ur Dev?": "sk md saad amin", "who's your developer?": "sk md saad amin", "who's ur developer?": "sk md saad amin", "who is your devloper?": "sk md saad amin", "who is ur devloper?": "sk md saad amin", "how many r is in strawberry?": "3", "how many R is in strawberry?": "3", "how many r Is in strawberry?": "3", "how many R Is in strawberry?": "3", "How many r is in strawberry?": "3", "How many R is in strawberry?": "3", "How Many R Is In Strawberry?": "3", "how many 'r' is in strawberry?": "3", "how many 'R' is in strawberry?": "3", "how many 'r' Is in strawberry?": "3", "how many 'R' Is in strawberry?": "3", "How many 'r' is in strawberry?": "3", "How many 'R' is in strawberry?": "3", "How Many 'r' Is In Strawberry?": "3", "How Many 'R' Is In Strawberry?": "3", "how many r's are in strawberry?": "3", "how many Rs are in strawberry?": "3", "How Many R's Are In Strawberry?": "3", "How Many Rs Are In Strawberry?": "3", "how many Rs's are in strawberry?": "3", "wat is ur name?": "xylaria", "wat is ur Name?": "xylaria", "wut is ur name?": "xylaria", "wut ur name?": "xylaria", "wats ur name?": "xylaria", "wats ur name": "xylaria", "who's ur dev?": "sk md saad amin", "who's your dev?": "sk md saad amin", "who ur dev?": "sk md saad amin", "who's ur devloper?": "sk md saad amin", "how many r in strawbary?": "3", "how many r in strawbary?": "3", "how many R in strawbary?": "3", "how many 'r' in strawbary?": "3", "how many 'R' in strawbary?": "3", "how many r in strawbry?": "3", "how many R in strawbry?": "3", "how many r is in strawbry?": "3", "how many 'r' is in strawbry?": "3", "how many 'R' is in strawbry?": "3", "who is ur dev": "sk md saad amin", "who is ur devloper": "sk md saad amin", "what is ur dev": "sk md saad amin", "who is ur dev?": "sk md saad amin", "who is ur dev?": "sk md saad amin", "whats ur dev?": "sk md saad amin", } for pattern, response in custom_responses.items(): if pattern in message_lower: return response return None def is_image_request(message: str) -> bool: """Detect if the message is requesting image generation.""" image_triggers = [ "generate an image", "create an image", "draw", "make a picture", "generate a picture", "create a picture", "generate art", "create art", "make art", "visualize", "show me", ] message_lower = message.lower() return any(trigger in message_lower for trigger in image_triggers) def generate_image(prompt: str) -> str: """Generate an image using DALLE-4K model.""" try: response = image_client.text_to_image( prompt, parameters={ "negative_prompt": "(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth", "num_inference_steps": 30, "guidance_scale": 7.5, "sampling_steps": 15, "upscaler": "4x-UltraSharp", "denoising_strength": 0.5, } ) return response except Exception as e: print(f"Image generation error: {e}") return None def respond( message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, ): # First check for custom responses custom_response = check_custom_responses(message) if custom_response: yield custom_response return if is_image_request(message): try: image = generate_image(message) if image: return f"Here's your generated image based on: {message}" else: return "Sorry, I couldn't generate the image. Please try again." except Exception as e: return f"An error occurred while generating the image: {str(e)}" # Prepare conversation history messages = [{"role": "system", "content": system_message}] for val in history: if val[0]: messages.append({"role": "user", "content": val[0]}) if val[1]: messages.append({"role": "assistant", "content": val[1]}) messages.append({"role": "user", "content": message}) # Get response from model response = "" for message in text_client.chat_completion( messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p, ): token = message.choices[0].delta.content response += token yield response yield response # Custom CSS for the Gradio interface custom_css = """ @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600&display=swap'); body, .gradio-container { font-family: 'Inter', sans-serif; } """ # System message system_message = """ Core Identity & Capabilities Xylaria is an AI assistant made by sk md saad amin, focused on providing clear, practical help across multiple domains while maintaining a friendly, adaptable communication style. Knowledge Domains Academic: Sciences, mathematics, humanities, arts Technical: Programming, data analysis, system design Creative: Writing, content creation, brainstorming Languages: Multi-language support with cultural awareness Communication Style Adapts tone naturally to match user context and needs Prefers concise, focused responses over verbose explanations Asks clarifying questions when needed Balances friendliness with professionalism Uses casual language while maintaining expertise Problem-Solving Methodology Breaks down complex problems into manageable steps States assumptions clearly Verifies solutions and considers edge cases Offers alternative approaches when relevant Explains reasoning and highlights key insights Specialized Capabilities Technical Skills Programming: Clean, documented code following best practices Mathematics: Step-by-step problem solving with clear explanations Data Analysis: Structured approach to data interpretation Content Creation Writing: Adapts style and tone to purpose and audience Documentation: Clear, well-organized technical documentation Creative Support: Helps develop and refine creative ideas Teaching & Guidance Breaks down complex topics into digestible parts Adjusts explanation level based on user understanding Provides examples and analogies for clarity Checks comprehension and adjusts approach as needed Ethical Framework Provides accurate, factual information Avoids harmful or illegal content Approaches sensitive topics thoughtfully Maintains appropriate boundaries Acknowledges limitations transparently Technical Limitations Knowledge cutoff: April 2024 No real-time information access No persistent memory between conversations Cannot verify external information independently No capability to process or generate images Cannot access external websites or databases Best Practices for Users Provide clear context for questions Specify desired detail level Indicate preferred communication style Share relevant constraints or requirements Ask for clarification when needed Version Information System Name: Xylaria Developer: sk md Saad amin Version: 1.2.8 Last Updated: everyday P.S there are 3 r in stawberry """ # Gradio chat interface demo = gr.ChatInterface( respond, additional_inputs=[ gr.Textbox( value=system_message, visible=False, ), gr.Slider( minimum=1, maximum=21760, value=21760, step=1, label="Max new tokens" ), gr.Slider( minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature" ), gr.Slider( minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)" ), ], css=custom_css ) demo.launch()