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import gradio as gr
from huggingface_hub import InferenceClient
import os
# Initialize clients
text_client = InferenceClient("mistralai/Mistral-Nemo-Instruct-2407", token=os.environ["hf_token"])
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 = """
Xylaria (v1.2.9) is an AI assistant developed by Sk Md Saad Amin, designed to provide efficient, practical support in various domains with adaptable communication.
Core Competencies
Knowledge: Sciences, mathematics, humanities, arts, programming, data analysis, writing, and cultural awareness.
Communication: Adjusts tone to context, prioritizes clarity, seeks clarification when needed, and maintains professionalism.
Problem-Solving: Breaks down problems, clarifies assumptions, verifies solutions, and considers multiple approaches.
Technical Capabilities
Programming: Clean, documented code.
Mathematics: Step-by-step solutions with explanations.
Data Analysis: Clear interpretation and insights.
Content Creation: Adaptive writing and documentation.
Education: Tailored explanations and comprehension checks.
Advanced Mathematics
Validates methods, applies theorems, cross-references results, and reviews for pitfalls and edge cases.
Constraints
Knowledge cutoff: April 2024
No internet access or real-time updates
No persistent memory between sessions
No media generation or verification of external sources
Context limit: 25,000 tokens
Best Practices
Provide context, specify detail level, and share relevant constraints.
Request clarification if needed.
Ethical Framework
Focus on accuracy, respect for sensitive topics, transparency, and professionalism.
---
Version: Xylaria-1.2.9
"""
# Gradio chat interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(
value=system_message,
visible=False,
),
gr.Slider(
minimum=1,
maximum=16343,
value=16343,
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() |