"""
OpenRouter Chatbot – elenco dinamico completo
Run: gradio app.py
"""

import os
import functools
import requests
import gradio as gr

# ------------------------------------------------------------------
# Configurazione
# ------------------------------------------------------------------
OPENROUTER_API_KEY = os.getenv(
    "OPENROUTER_API_KEY",
    "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"
)

# ------------------------------------------------------------------
# Utility per ottenere l’elenco modelli
# ------------------------------------------------------------------
@functools.lru_cache(maxsize=1)
def fetch_models() -> list[str]:
    """
    Restituisce la lista completa di modelli offerti da OpenRouter.
    Il decorator @lru_cache la memorizza in cache per tutta la durata
    del processo (si aggiorna solo al riavvio del container).
    """
    headers = {"Authorization": f"Bearer {OPENROUTER_API_KEY}"}
    try:
        resp = requests.get(
            "https://openrouter.ai/api/v1/models",
            headers=headers,
            timeout=15
        )
        resp.raise_for_status()
        data = resp.json()
        models = sorted(m["id"] for m in data["data"])
        return models
    except Exception as e:
        gr.Warning(f"Impossibile scaricare l’elenco modelli: {e}")
        return ["openai/gpt-4-turbo"]  # fallback statico

# ------------------------------------------------------------------
# Funzione di chiamata al modello
# ------------------------------------------------------------------
def chat_with_openrouter(prompt: str, model: str):
    headers = {
        "Authorization": f"Bearer {OPENROUTER_API_KEY}",
        "Content-Type": "application/json"
    }

    payload = {
        "model": model,
        "messages": [{"role": "user", "content": prompt}],
        "max_tokens": 4096,
        "temperature": 0.7,
    }

    try:
        resp = requests.post(
            "https://openrouter.ai/api/v1/chat/completions",
            headers=headers,
            json=payload,
            timeout=60
        )
        resp.raise_for_status()
        return resp.json()["choices"][0]["message"]["content"]
    except Exception as e:
        return f"❌ Errore: {e}"

# ------------------------------------------------------------------
# Interfaccia Gradio
# ------------------------------------------------------------------
def build_interface():
    models = fetch_models()

    with gr.Blocks(title="NotExistChatter – Tutti i modelli") as demo:
        gr.Markdown("🤖project Adam🤖")
        gr.Markdown("Il menù mostra **tutti** i modelli disponibili.")
        with gr.Row():
            model_dropdown = gr.Dropdown(
                choices=models,
                value=models[0] if models else None,
                label="Modello",
                allow_custom_value=False,
                interactive=True
            )

        prompt_box = gr.Textbox(
            label="Prompt",
            placeholder="Scrivi qui il tuo messaggio...",
            lines=4,
            max_lines=10
        )

        output_box = gr.Textbox(
            label="Risposta",
            interactive=False,
            lines=15,
            max_lines=20
        )

        send_btn = gr.Button("Invia", variant="primary")
        send_btn.click(
            fn=chat_with_openrouter,
            inputs=[prompt_box, model_dropdown],
            outputs=output_box
        )

        prompt_box.submit(
            fn=chat_with_openrouter,
            inputs=[prompt_box, model_dropdown],
            outputs=output_box
        )

    return demo

# Lancia l’interfaccia
if __name__ == "__main__":
    build_interface().launch()