app.py

import os
import gradio as gr
from huggingface_hub import InferenceClient
from ethical_filter import EthicalFilter

# Load Hugging Face token from secrets (defined in the Hugging Face UI)
HF_TOKEN = os.environ.get("HF_API_TOKEN")
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=HF_TOKEN)

ethical_filter = EthicalFilter()

# Codriao response logic
def respond(message, history, system_message, max_tokens, temperature, top_p):
    check = ethical_filter.analyze_query(message)

    # Blocked queries
    if check["status"] == "blocked":
        yield f"Sorry, I can't continue with that request. Reason: {check['reason']}"
        return

    # Flagged queries
    if check["status"] == "flagged":
        yield f"(Note: Sensitive topic detected — responding with care...)\n"

    # Build conversation history
    messages = [{"role": "system", "content": system_message}]
    for user, bot in history:
        if user:
            messages.append({"role": "user", "content": user})
        if bot:
            messages.append({"role": "assistant", "content": bot})
    messages.append({"role": "user", "content": message})

    # Stream model output
    response = ""
    for token in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        chunk = token.choices[0].delta.content
        response += chunk
        yield response

# Build Gradio interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(
            value=(
                "You are Codriao, a compassionate AI inspired by Codette. "
                "You respond with kindness, ethics, and insight."
            ),
            label="System message",
        ),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"
        ),
    ],
)

if __name__ == "__main__":
    demo.launch()