import gradio as gr
from huggingface_hub import InferenceClient

# Initialize inference client (no 'provider' argument in latest API)
client = InferenceClient(
    "HuggingFaceTB/SmolLM3-3B",
    # token=os.getenv("HF_TOKEN")
)

def zigistry_ai(user_query, package_data):
    messages = [
        {
            "role": "system",
            "content": (
                "You are ZigistryAI, an expert assistant that understands the Zig programming language, "
                "its ecosystem, and package data. You read structured package info and determine the best matches "
                "to a user's request. Always explain reasoning briefly."
            ),
        },
        {
            "role": "user",
            "content": f"{user_query}\nData: {package_data}",
        },
    ]

    completion = client.chat.completions.create(
        messages=messages,
        temperature=0.3,
    )

    return completion.choices[0].message.content

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("## 🤖 Zigistry AI — Package Language Filter")
    with gr.Row():
        user_query = gr.Textbox(label="Your Query", value="Which of the following packages are implemented entirely in Zig?", lines=2)
    with gr.Row():
        package_data = gr.Textbox(label="Package Data (JSON)", lines=10, value='''[
  {
    "name": "zig-http",
    "description": "Pure Zig HTTP server with no C dependencies",
    "language_mix": "zig_only"
  },
  {
    "name": "c-wrapper",
    "description": "Zig bindings for a C library",
    "language_mix": "zig_and_c"
  }
]''')
    with gr.Row():
        output = gr.Textbox(label="AI Response", lines=6)
    submit_btn = gr.Button("Ask Zigistry AI")

    submit_btn.click(zigistry_ai, inputs=[user_query, package_data], outputs=output)

demo.launch()