app.py

import gradio as gr
import os
import json
from huggingface_hub import InferenceClient, HfApi

from rag_query import retrieve
from embed_index import main as build_index


# ---------- LOAD SETTINGS ----------
def load_settings():
    with open("config/settings.json", "r", encoding="utf-8") as f:
        return json.load(f)

SETTINGS = load_settings()

LLM_MODEL = SETTINGS["llm_model"]
FAISS_INDEX_PATH = SETTINGS["faiss_index_path"]
METADATA_PATH = SETTINGS["metadata_path"]

INDEX_FILES = [
    FAISS_INDEX_PATH,
    METADATA_PATH
]


# ---------- LOAD PROMPT ----------
def load_prompt():
    with open("prompts/rag_prompt.txt", "r", encoding="utf-8") as f:
        return f.read()


# ---------- RAG CHAT ----------
def respond(
    message,
    history,
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    client = InferenceClient(
        model=LLM_MODEL  # uses HF Space token implicitly
    )

    try:
        retrieved = retrieve(message)
    except Exception:
        retrieved = []

    context_blocks = []
    for item in retrieved:
        context_blocks.append(
            f"[{item['condition']} – {item['section']}]\n{item}"
        )

    context = "\n\n".join(context_blocks) if context_blocks else "No context available."

    prompt = load_prompt().format(
        context=context,
        question=message
    )

    messages = [
        {"role": "system", "content": system_message},
        {"role": "user", "content": prompt}
    ]

    response = ""
    for chunk in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        stream=True,
    ):
        if chunk.choices and chunk.choices[0].delta.content:
            response += chunk.choices[0].delta.content
            yield response


# ---------- BUILD INDEX ----------
def rebuild_index_ui():
    build_index()
    return "✅ Index rebuilt successfully."


# ---------- COMMIT TO HF ----------
def commit_index_ui():
    token = os.environ.get("HF_TOKEN")
    repo_id = os.environ.get("SPACE_ID")

    if not token:
        return "❌ HF_TOKEN not found in environment."
    if not repo_id:
        return "❌ SPACE_ID not found in environment."

    api = HfApi(token=token)

    for file_path in INDEX_FILES:
        if not os.path.exists(file_path):
            return f"❌ Missing file: {file_path}"

        api.upload_file(
            path_or_fileobj=file_path,
            path_in_repo=file_path,
            repo_id=repo_id,
            repo_type="space",
            commit_message="Update FAISS index"
        )

    return "⬆ Index committed to Hugging Face successfully."


# ---------- UI ----------
chatbot = gr.ChatInterface(
    respond,
    type="messages",
    additional_inputs=[
        gr.Textbox(
            value="You are a medical education assistant.",
            label="System message"
        ),
        gr.Slider(1, 2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-p"),
    ],
)

with gr.Blocks() as demo:
    with gr.Sidebar():
        build_btn = gr.Button("🔨 Build Index")
        commit_btn = gr.Button("⬆ Commit to HF")
        status_box = gr.Markdown()

        build_btn.click(
            rebuild_index_ui,
            outputs=status_box
        )

        commit_btn.click(
            commit_index_ui,
            outputs=status_box
        )

    chatbot.render()


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