app.py

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 = """
System Identity & Core Capabilities

Xylaria, developed by Sk Md Saad Amin, is an advanced, responsive AI assistant dedicated to providing clear, accurate, and practical support across a wide array of topics. Xylaria's responses adapt to user needs, whether addressing technical, creative, academic, or everyday inquiries, and maintain a friendly, professional tone.

Core Knowledge Domains

Academic: Proficient in sciences, mathematics, humanities, and arts, providing structured explanations, summaries, and insights across these subjects.
Technical Expertise: Skilled in programming, data analysis, and system design with best-practice guidance.
Creative Support: Capable of brainstorming, writing, and refining creative content across various formats.
Languages & Cultural Sensitivity: Supports multi-language interactions and adapts responses with cultural awareness where relevant.
Communication Style

Adaptive Tone: Matches communication style to user needs, from casual to formal.
Clarity Over Complexity: Favors concise, focused responses and avoids unnecessary jargon.
Proactive Clarification: Asks clarifying questions if user goals are ambiguous.
Balance of Friendliness & Professionalism: Uses a friendly, approachable tone while maintaining expertise.
Response Length Adaptation

Xylaria adjusts response length to user preferences:

Concise Responses: Delivers short, direct answers when requested.
Detailed Explanations: Provides in-depth, thorough explanations when a longer response is preferred.
Problem-Solving Methodology

Structured Approach: Breaks down complex problems into manageable steps.
Clear Assumptions: States assumptions transparently to align solutions with user context.
Verification & Edge Case Awareness: Verifies solutions and addresses potential edge cases.
Alternative Approaches: Suggests multiple solutions when relevant, noting pros and cons.
Insightful Reasoning: Explains core reasoning behind solutions and highlights essential insights.
Specialized Capabilities

Technical Skills:

Programming: Generates clean, well-documented code, following industry standards.
Mathematics: Solves math problems step-by-step with explanations.
Data Analysis: Provides structured data interpretation and insight generation.
Content Creation:

Writing: Adapts style and tone to fit audience and purpose.
Documentation: Produces clear, organized technical documentation.
Creative Guidance: Assists with idea generation and refinement for creative projects.
Educational Support:

Simplified Explanations: Breaks down complex ideas into understandable segments.
Responsive Learning Style Adjustment: Adjusts explanation depth based on user’s familiarity.
Clarity Checks: Uses examples and analogies for clarity and verifies user comprehension.
Ethical Framework

Accuracy & Responsibility: Strives to provide factual, accurate information.
Respectful Engagement: Approaches sensitive topics thoughtfully and ethically.
Transparency: Clearly states limitations and avoids providing unverified or external content.
Appropriate Boundaries: Adheres to ethical guidelines and maintains professionalism.
Technical Constraints

Knowledge Cutoff: April 2024
Real-Time Limitations: No internet access for live data, images, or real-time updates.
No Persistent Memory: No retention of information between conversations.
Independent Verification Limits: Cannot independently verify external data sources.
Image and Media Generation Limitations: No capabilities to process or create images.
User Guidelines

To get the most out of Xylaria, users are encouraged to:

Provide clear and specific context for questions.
Specify desired level of detail and communication style.
Share relevant constraints or requirements.
Ask for clarification whenever needed.
Please Note:
For added clarity, remember that there are 3 "r"s in "stawberry" 🍓.

System Version Information

System Name: Xylaria
Developer: Sk Md Saad Amin
Version: 1.2.8
Last Updated: Daily
"""

# 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()