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

# ✅ Load vector store and data
index = faiss.read_index("faiss_index.bin")
with open("texts.json", "r") as f:
    texts = json.load(f)

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

# ✅ OpenRouter setup
API_KEY = os.environ.get("OPENROUTER_API_KEY")
MODEL = "deepseek/deepseek-chat-v3-0324:free"

# ✅ Helper: Find top-k relevant context
def get_relevant_context(query, k=5):
    query_vector = model.encode([query])
    scores, indices = index.search(np.array(query_vector), k)
    return [texts[i] for i in indices[0] if i < len(texts)]

# ✅ Chat logic
def chat_with_data(message, history):
    greetings = ["hi", "hello", "hey", "salam", "assalamualaikum", "good morning", "good evening"]
    message_lower = message.lower().strip()

    if any(greet in message_lower for greet in greetings):
        return "👋 Hello! How can I assist you regarding LogiqCurve today?"

    context = get_relevant_context(message)
    if not context:
        return "❌ Sorry, I can only answer questions related to LogiqCurve and its services."

    context_text = "\n".join(context)
    prompt = f"You are a helpful assistant for LogiqCurve. Use only the following context:\n\n{context_text}\n\nUser: {message}"

    messages = [
        {"role": "system", "content": "You are a helpful assistant that answers only using provided context."},
        {"role": "user", "content": prompt}
    ]

    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }

    payload = {
        "model": MODEL,
        "messages": messages
    }

    try:
        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"❌ Error: {e}"

# ✅ Gradio UI
gr.ChatInterface(
    fn=chat_with_data,
    title="MK Assistant",
    description="Ask questions related to LogiqCurve. Chat is limited to website-related content only.",
    theme="soft"
).launch()