from webscout import Meta

ai = Meta()
response = ai.chat("Explain quantum computing in simple terms")
print(response)

from webscout import DuckDuckGoSearch

search = DuckDuckGoSearch()
results = search.text("best practices for API design", max_results=5)
for result in results:
    print(f"{result['title']}: {result['href']}")

from webscout.Provider.TTI import PollinationsAI

gen = PollinationsAI()
path = gen.generate_image(prompt="A serene mountain landscape at sunset")
print(f"Saved to: {path}")

from webscout import Meta, Toolbaz, LLMChat, SonusAI, Netwrck, PiAI

ai = Meta()
print(ai.chat("What is the capital of France?"))

from webscout import OpenAI, GROQ, GEMINI, Cohere, DeepInfra

groq = GROQ(api_key="your-key")
response = groq.chat("Write a Python function to sort a list")

from webscout.Provider.OPENAI import ChatGPT, Groq, DeepInfra

chatgpt = ChatGPT()  # No auth required
response = chatgpt.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}]
)

from webscout import DuckDuckGoSearch, BingSearch, YepSearch, YahooSearch, BraveSearch

# DuckDuckGo
ddg = DuckDuckGoSearch()
results = ddg.text("python frameworks", max_results=5)

# Bing
bing = BingSearch()
results = bing.text("climate change solutions")

# Brave
brave = BraveSearch()
results = bravesearch.text("machine learning tutorials")

from webscout.Provider.TTI import PollinationsAI, TogetherImage

# PollinationsAI
poll = PollinationsAI()
poll.generate_image(prompt="A cyberpunk city at night")

# Together AI
together = TogetherImage()
together.generate_image(prompt="A robot playing chess")

from webscout.Provider.TTS import ElevenlabsTTS, ParlerTTS

tts = ElevenlabsTTS()
tts.text_to_speech("Hello, world!", voice="alloy")

from openai import OpenAI

client = OpenAI(api_key="dummy", base_url="http://localhost:8000/v1")

response = client.chat.completions.create(
    model="ChatGPT/gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

from webscout.client import Client

client = Client(print_provider_info=True)

# Auto provider + model selection
resp = client.chat.completions.create(
    model="auto",
    messages=[{"role": "user", "content": "Summarize Webscout."}]
)
print(resp.choices[0].message.content)

# Streaming
stream = client.chat.completions.create(
    model="ChatGPT/gpt-4o-mini",
    messages=[{"role": "user", "content": "Write a limerick about Python."}],
    stream=True,
)
for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

# Image generation
img = client.images.generate(prompt="A neon owl", model="auto", size="1024x1024")
print(img.data[0].url)

from webscout.Provider.Apriel import Apriel
from webscout.AIbase import Tool

def get_weather(city: str) -> str:
    return f"Weather in {city}: Sunny, 25C"

weather_tool = Tool(
    name="get_weather",
    description="Get current weather for a city.",
    parameters={"city": {"type": "string", "description": "City name."}},
    implementation=get_weather,
)

ai = Apriel(tools=[weather_tool])
print(ai.chat("What is the weather in London?"))

from webscout import model

# All LLM models
all_models = model.llm.list()
print(f"Total: {len(all_models)}")

# Models by provider
summary = model.llm.summary()
for provider, count in summary.items():
    print(f"  {provider}: {count}")

# TTS voices
voices = model.tts.list()
print(f"Total voices: {len(voices)}")