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"""
UI components for HF-Inferoxy AI Hub.
Contains functions to create different sections of the Gradio interface.
"""
import gradio as gr
from utils import (
DEFAULT_CHAT_MODEL, DEFAULT_IMAGE_MODEL, DEFAULT_IMAGE_PROVIDER,
CHAT_CONFIG, IMAGE_CONFIG, IMAGE_PROVIDERS, IMAGE_MODEL_PRESETS,
IMAGE_EXAMPLE_PROMPTS
)
def create_chat_tab(handle_chat_submit_fn):
"""
Create the chat tab interface.
"""
with gr.Tab("💬 Chat Assistant", id="chat"):
# Chat interface at the top - most prominent
chatbot_display = gr.Chatbot(
label="Chat",
type="messages",
height=800,
show_copy_button=True
)
# Chat input
with gr.Row():
chat_input = gr.Textbox(
placeholder="Type your message here...",
label="Message",
scale=4,
container=False
)
chat_submit = gr.Button("Send", variant="primary", scale=1)
# Configuration options below the chat
with gr.Row():
with gr.Column(scale=1):
chat_model_name = gr.Textbox(
value=DEFAULT_CHAT_MODEL,
label="Model Name",
placeholder="e.g., openai/gpt-oss-20b or openai/gpt-oss-20b:fireworks-ai"
)
chat_system_message = gr.Textbox(
value=CHAT_CONFIG["system_message"],
label="System Message",
lines=2,
placeholder="Define the assistant's personality and behavior..."
)
with gr.Column(scale=1):
chat_max_tokens = gr.Slider(
minimum=1, maximum=4096, value=CHAT_CONFIG["max_tokens"], step=1,
label="Max New Tokens"
)
chat_temperature = gr.Slider(
minimum=0.1, maximum=2.0, value=CHAT_CONFIG["temperature"], step=0.1,
label="Temperature"
)
chat_top_p = gr.Slider(
minimum=0.1, maximum=1.0, value=CHAT_CONFIG["top_p"], step=0.05,
label="Top-p (nucleus sampling)"
)
# Configuration tips below the chat
create_chat_tips()
# Connect chat events (streaming auto-detected from generator function)
chat_submit.click(
fn=handle_chat_submit_fn,
inputs=[chat_input, chatbot_display, chat_system_message, chat_model_name,
chat_max_tokens, chat_temperature, chat_top_p],
outputs=[chatbot_display, chat_input]
)
chat_input.submit(
fn=handle_chat_submit_fn,
inputs=[chat_input, chatbot_display, chat_system_message, chat_model_name,
chat_max_tokens, chat_temperature, chat_top_p],
outputs=[chatbot_display, chat_input]
)
def create_chat_tips():
"""Create the tips section for the chat tab."""
with gr.Row():
with gr.Column():
gr.Markdown("""
### 💡 Chat Tips
**Model Format:**
- Single model: `openai/gpt-oss-20b` (uses auto provider)
- With provider: `openai/gpt-oss-20b:fireworks-ai`
**Popular Models:**
- `openai/gpt-oss-20b` - Fast general purpose
- `meta-llama/Llama-2-7b-chat-hf` - Chat optimized
- `microsoft/DialoGPT-medium` - Conversation
- `google/flan-t5-base` - Instruction following
""")
with gr.Column():
gr.Markdown("""
### 🚀 Popular Providers
- **auto** - Let HF choose best provider (default)
- **fireworks-ai** - Fast and reliable
- **cerebras** - High performance
- **groq** - Ultra-fast inference
- **together** - Wide model support
- **cohere** - Advanced language models
**Examples:**
- `openai/gpt-oss-20b` (auto provider)
- `openai/gpt-oss-20b:fireworks-ai` (specific provider)
""")
def create_image_tab(handle_image_generation_fn):
"""
Create the image generation tab interface.
"""
with gr.Tab("🎨 Image Generator", id="image"):
with gr.Row():
with gr.Column(scale=2):
# Image output
output_image = gr.Image(
label="Generated Image",
type="pil",
height=600,
show_download_button=True
)
status_text = gr.Textbox(
label="Generation Status",
interactive=False,
lines=2
)
with gr.Column(scale=1):
# Model and provider inputs
with gr.Group():
gr.Markdown("**🤖 Model & Provider**")
img_model_name = gr.Textbox(
value=DEFAULT_IMAGE_MODEL,
label="Model Name",
placeholder="e.g., Qwen/Qwen-Image or stabilityai/stable-diffusion-xl-base-1.0"
)
img_provider = gr.Dropdown(
choices=IMAGE_PROVIDERS,
value=DEFAULT_IMAGE_PROVIDER,
label="Provider",
interactive=True
)
# Generation parameters
with gr.Group():
gr.Markdown("**📝 Prompts**")
img_prompt = gr.Textbox(
value=IMAGE_EXAMPLE_PROMPTS[0], # Use first example as default
label="Prompt",
lines=3,
placeholder="Describe the image you want to generate..."
)
img_negative_prompt = gr.Textbox(
value=IMAGE_CONFIG["negative_prompt"],
label="Negative Prompt",
lines=2,
placeholder="Describe what you DON'T want in the image..."
)
with gr.Group():
gr.Markdown("**⚙️ Generation Settings**")
with gr.Row():
img_width = gr.Slider(
minimum=256, maximum=2048, value=IMAGE_CONFIG["width"], step=64,
label="Width", info="Must be divisible by 8"
)
img_height = gr.Slider(
minimum=256, maximum=2048, value=IMAGE_CONFIG["height"], step=64,
label="Height", info="Must be divisible by 8"
)
with gr.Row():
img_steps = gr.Slider(
minimum=10, maximum=100, value=IMAGE_CONFIG["num_inference_steps"], step=1,
label="Inference Steps", info="More steps = better quality"
)
img_guidance = gr.Slider(
minimum=1.0, maximum=20.0, value=IMAGE_CONFIG["guidance_scale"], step=0.5,
label="Guidance Scale", info="How closely to follow prompt"
)
img_seed = gr.Slider(
minimum=-1, maximum=999999, value=IMAGE_CONFIG["seed"], step=1,
label="Seed", info="-1 for random"
)
# Generate button
generate_btn = gr.Button(
"🎨 Generate Image",
variant="primary",
size="lg",
scale=2
)
# Quick model presets
create_image_presets(img_model_name, img_provider)
# Examples for image generation
create_image_examples(img_prompt)
# Connect image generation events
generate_btn.click(
fn=handle_image_generation_fn,
inputs=[
img_prompt, img_model_name, img_provider, img_negative_prompt,
img_width, img_height, img_steps, img_guidance, img_seed
],
outputs=[output_image, status_text]
)
def create_image_presets(img_model_name, img_provider):
"""Create quick model presets for image generation."""
with gr.Group():
gr.Markdown("**🎯 Popular Presets**")
for name, model, provider in IMAGE_MODEL_PRESETS:
btn = gr.Button(name, size="sm")
btn.click(
lambda m=model, p=provider: (m, p),
outputs=[img_model_name, img_provider]
)
def create_image_examples(img_prompt):
"""Create example prompts for image generation."""
with gr.Group():
gr.Markdown("**🌟 Example Prompts**")
img_examples = gr.Examples(
examples=[[prompt] for prompt in IMAGE_EXAMPLE_PROMPTS],
inputs=img_prompt
)
def create_main_header():
"""Create the main header for the application."""
gr.Markdown("""
# 🚀 HF-Inferoxy AI Hub
A comprehensive AI platform combining chat and image generation capabilities with intelligent token management through HF-Inferoxy.
**Features:**
- 💬 **Smart Chat**: Conversational AI with streaming responses
- 🎨 **Image Generation**: Text-to-image creation with multiple providers
- 🔄 **Intelligent Token Management**: Automatic token rotation and error handling
- 🌐 **Multi-Provider Support**: Works with HF Inference, Cerebras, Cohere, Groq, Together, Fal.ai, and more
""")
def create_footer():
"""Create the footer with helpful information."""
gr.Markdown("""
---
### 📚 How to Use
**Chat Tab:**
- Enter your message and customize the AI's behavior with system messages
- Choose models and providers using the format `model:provider`
- Adjust temperature for creativity and top-p for response diversity
**Image Tab:**
- Write detailed prompts describing your desired image
- Use negative prompts to avoid unwanted elements
- Experiment with different models and providers for varied styles
- Higher inference steps = better quality but slower generation
**Supported Providers:**
- **fal-ai**: High-quality image generation (default for images)
- **hf-inference**: Core API with comprehensive model support
- **cerebras**: High-performance inference
- **cohere**: Advanced language models with multilingual support
- **groq**: Ultra-fast inference, optimized for speed
- **together**: Collaborative AI hosting, wide model support
- **nebius**: Cloud-native services with enterprise features
- **nscale**: Optimized inference performance
- **replicate**: Collaborative AI hosting
**Built with ❤️ using [HF-Inferoxy](https://nazdridoy.github.io/hf-inferoxy/) for intelligent token management**
""")
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