Aidatainsights Cast Update

import requests
import os

API_KEY = os.environ.get("GLOBAL_API_KEY")
BASE_URL = "https://global-apis.com/v1"

def chat(model, messages, temperature=0.7, max_tokens=512):
    """
    Send a chat completion request through the unified API.
    Same call structure works for 184+ models.
    """
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": model,
        "messages": messages,
        "temperature": temperature,
        "max_tokens": max_tokens
    }
    response = requests.post(
        f"{BASE_URL}/chat/completions",
        headers=headers,
        json=payload,
        timeout=30
    )
    response.raise_for_status()
    return response.json()

# Example 1: Cheap classification with a small model
result_small = chat(
    model="llama-3.2-3b-instruct",
    messages=[{"role": "user", "content": "Classify sentiment: 'I love this product!'"}]
)
print("Small model:", result_small["choices"][0]["message"]["content"])

# Example 2: Complex reasoning with a frontier model
result_frontier = chat(
    model="claude-sonnet-4",
    messages=[{"role": "user", "content": "Explain why P != NP matters in plain English."}],
    max_tokens=1024
)
print("Frontier model:", result_frontier["choices"][0]["message"]["content"])

# Example 3: Streaming with a mid-tier model
import json
def stream_chat(model, messages):
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": model,
        "messages": messages,
        "stream": True
    }
    with requests.post(
        f"{BASE_URL}/chat/completions",
        headers=headers,
        json=payload,
        stream=True,
        timeout=60
    ) as r:
        for line in r.iter_lines():
            if line:
                line = line.decode("utf-8")
                if line.startswith("data: "):
                    data = line[6:]
                    if data != "[DONE]":
                        chunk = json.loads(data)
                        delta = chunk["choices"][0]["delta"].get("content", "")
                        if delta:
                            print(delta, end="", flush=True)

# stream_chat("qwen-2.5-72b-instruct", [{"role": "user", "content": "Write a haiku about APIs."}])