import gradio as gr
import faiss
import numpy as np
import json
from sentence_transformers import SentenceTransformer
import os

# Load your content and index
with open("texts.json", "r") as f:
    texts = json.load(f)

index = faiss.read_index("faiss_index.bin")

# Load embedding model
embed_model = SentenceTransformer("all-MiniLM-L6-v2")

# Helper to retrieve relevant chunks
def retrieve_chunks(query, k=5):
    query_emb = embed_model.encode([query])
    D, I = index.search(np.array(query_emb), k)
    return [texts[i] for i in I[0] if i < len(texts)]

# Main chatbot function
def rag_chatbot(message, history, request: gr.Request):
    user_header = request.headers.get("X-Source", "")
    if user_header == "wordpress":
        # RAG logic for WordPress embed
        context_chunks = retrieve_chunks(message)
        context = "\n".join(context_chunks)
        if not context.strip():
            return "❌ I'm sorry, I can only assist with questions related to LogiqCurve.com."
        prompt = f"Answer based ONLY on this content:\n{context}\n\nQuestion: {message}"
    else:
        # Public fallback (general AI)
        prompt = message

    # Use OpenRouter API
    headers = {
        "Authorization": f"Bearer {os.environ.get('OPENROUTER_API_KEY')}",
        "Content-Type": "application/json"
    }

    payload = {
        "model": "deepseek/deepseek-chat-v3-0324:free",
        "messages": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ]
    }

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

# Gradio interface
iface = gr.ChatInterface(
    fn=rag_chatbot,
    title="LogiqCurve WordPress Assistant",
    description="Answers only based on the content of LogiqCurve.com",
    theme="soft"
)

iface.launch()