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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() |