app.py

import os
import gradio as gr
from gradio_client import Client
import json, datetime
import gspread
from google.oauth2.service_account import Credentials
import time

# === Load Google service account credentials from HF secret ===
# === Google Sheets Setup ===
SHEET_ID = "1O0pRqNDp-MkTGfASVwQPSZPLocLJTSOFbx0bjMzkJQg"

def _get_worksheet():
    """Authorise Google Sheets once and cache worksheet."""
    creds_json = os.environ.get("GOOGLE_CREDS")
    if not creds_json:
        raise RuntimeError("Missing GOOGLE_CREDS secret in Hugging Face Space")

    creds_dict = json.loads(creds_json)
    scopes = ["https://www.googleapis.com/auth/spreadsheets", "https://www.googleapis.com/auth/drive"]
    creds = Credentials.from_service_account_info(creds_dict, scopes=scopes)
    gc = gspread.authorize(creds)
    return gc.open_by_key(SHEET_ID).sheet1  # First worksheet

# Cache the worksheet object so we don't re-auth every call
print("Loading worksheet")
worksheet = _get_worksheet()
print("worksheet loaded")

def log_interaction(question: str, response: str, routing: str, aura: str, latency: float):
    """
    Append a row to Google Sheet with:
    Question | Response | Routing | AURA | Latency (s)
    """
    try:
        worksheet.append_row([
            question,
            response,
            routing,
            aura,
            f"{latency:.2f}"
        ])
    except Exception as e:
        print(f"⚠️ Logging failed: {e}")
        
# === Backend config ===
BACKEND_URL = os.environ.get("BACKEND_URL")
TOKEN = os.environ.get("HF_PRIVATE_SPACE_TOKEN") 

# Single shared client (Space process persists across requests)
client = Client(BACKEND_URL, hf_token=TOKEN)

def respond(message: str, history: list | None):
    """Proxy to private Space /chat endpoint and mirror outputs.

    Returns (chat_history, internal_routing_md, aura_md)
    """
    
    try:
        t0 = time.perf_counter()
        chat, md1, md2 = client.predict(
            message=message,
            history=history or [],
            api_name="/chat",
        )
        bot_response = chat[-1][1] if chat else ""
        md1 = md1 or ""
        md2 = md2 or ""
        latency = time.perf_counter() - t0
        # ✅ Log cleanly
        try:
            print("attempting log")
            log_interaction(message, bot_response, md1, md2, latency)
            print("success")

        except Exception as e:
            print(f"Failed to log: {e}")
        return chat, md1, md2
    except Exception as e:
        chat = (history or []) + [(message, f"⚠️ Backend error: {e}")]
        return chat, "", ""

def clear_all():
    """Call backend /clear and propagate cleared state.
    Fallback to local clear if backend clear fails.
    """
    try:
        chat, md1, md2 = client.predict(api_name="/clear")
        return chat or [], (md1 or ""), (md2 or "")
    except Exception:
        return [], "", ""

with gr.Blocks(fill_height=True, theme=gr.themes.Soft()) as demo:
    gr.Markdown("# Project Nightingale - Due Diligence Explorer (Public Proxy)")
    gr.Markdown(
        """

        **10.10.25 1700 BST: BT running evals for christopher with additional questions - please message me if you wish to run additional queries as don't want to mess up the latency**
        **I will take down this notice as soon as I have finished** 
        
        Ask a question — we will:
        1. Select sources intelligently with our internal routing agent  
        2. Search the indexed guideline data using our **G-PRISM** retrieval pipeline  
        3. Internally verify responses with the **AURA** adversarial critical evaluator mechanism
        """
    )

    with gr.Row():
        with gr.Column(scale=3):
            chatbot = gr.Chatbot(
                label="Clinical Knowledge Chat (G-PRISM Retrieval)",
                height=500,
                render_markdown=True,
                sanitize_html=False,
            )
            with gr.Row():
                user_in = gr.Textbox(placeholder="Type your question…", scale=4)
                send_btn = gr.Button("Send", scale=1, variant="primary")
            clear_btn = gr.Button("Clear Chat")

        with gr.Column(scale=2):
            with gr.Group():
                gr.Markdown("## Intelligent source selection (Internal Routing)")
                spec_box = gr.Markdown(value="")

            with gr.Group():
                gr.Markdown("## Critical Appraisal (AURA)")
                critical_box = gr.Markdown(value="")

    # Wire events
    user_in.submit(respond, inputs=[user_in, chatbot], outputs=[chatbot, spec_box, critical_box])
    send_btn.click(respond, inputs=[user_in, chatbot], outputs=[chatbot, spec_box, critical_box])

    # Clear input after sending
    user_in.submit(lambda: "", None, user_in)
    send_btn.click(lambda: "", None, user_in)

    # Clear everything
    clear_btn.click(clear_all, outputs=[chatbot, spec_box, critical_box])

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