File size: 4,039 Bytes
28d0df3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import gradio as gr
from huggingface_hub import InferenceClient
import os
CHAT_MODEL = "Qwen/Qwen2.5-7B-Instruct-1M:featherless-ai"
HF_API_KEY = os.getenv("HF_API_KEY")
# ── Tab 1: Get weather ────────────────────────────────────────────────────────────
def get_weather(location: str) -> str:
"""
Retrieves the current weather for a given location.
Args:
location: The city or place for which to fetch the weather.
"""
# In a real tool, call a weather API here.
return f"The weather in {location} is sunny with mild temperatures."
tab1 = gr.Interface(
fn=get_weather,
inputs=[
gr.Textbox(label="Location", placeholder="e.g. Brussels"),
],
outputs=gr.Textbox(label="Weather"),
title="Get Weather",
examples=[["Brussels"], ["London"]],
)
# ── Tab 2: Ask an LLM ─────────────────────────────────────────────────────────
llm_client = InferenceClient(model=CHAT_MODEL, api_key=HF_API_KEY)
def ask_llm(prompt, system_prompt, max_tokens):
response = llm_client.chat.completions.create(
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt},
],
max_tokens=int(max_tokens),
)
return response.choices[0].message.content
tab2 = gr.Interface(
fn=ask_llm,
inputs=[
gr.Textbox(label="Your question", lines=3),
gr.Textbox(label="System prompt", value="You are a helpful assistant.", lines=2),
gr.Slider(50, 500, value=200, step=50, label="Max tokens"),
],
outputs=gr.Textbox(label="Answer", lines=8),
title="Ask an LLM",
)
# ── Tab 3: Image Generator ────────────────────────────────────────────────────
img_client = InferenceClient("black-forest-labs/FLUX.1-schnell", api_key=HF_API_KEY)
def generate_image(prompt, style):
"""
Generate an image from a text description using Imagen via Google GenAI.
Parameters
----------
prompt : str
A natural-language description of the image to generate.
Returns
-------
PIL.Image.Image
The generated image as a Pillow Image object, ready to display in Gradio.
"""
full_prompt = f"{prompt}, {style} style" if style else prompt
return img_client.text_to_image(full_prompt)
tab3 = gr.Interface(
fn=generate_image,
inputs=[
gr.Textbox(label="Describe your image", placeholder="A futuristic city at sunset...", lines=2),
gr.Radio(
["photorealistic", "watercolor painting", "pixel art", "pencil sketch"],
label="Style",
value="photorealistic",
),
],
outputs=gr.Image(label="Generated image"),
title="Image Generator",
)
# ── Tab 4: Multi-turn Chat ────────────────────────────────────────────────────
chat_client = InferenceClient(model=CHAT_MODEL, api_key=HF_API_KEY)
def chat_fn(message, history):
messages = history + [{"role": "user", "content": message}]
response = chat_client.chat.completions.create(messages=messages)
return response.choices[0].message.content
tab4 = gr.ChatInterface(
fn=chat_fn,
title="Chat with Qwen",
examples=["Tell me a joke", "Explain AI in simple terms", "What is Gradio?"],
)
# ── Assemble ──────────────────────────────────────────────────────────────────
app = gr.TabbedInterface(
[tab1, tab2, tab3, tab4],
tab_names=["Get Weather", "Ask LLM", "Image Gen", "Chat"],
)
app.launch(mcp_server=True) |