app.py

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

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

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

# OpenRouter API Key (Set as secret in Hugging Face Space settings)
API_KEY = os.environ.get("OPENROUTER_API_KEY")
MODEL = "deepseek/deepseek-chat-v3-0324:free"

# Perform semantic search using FAISS
def search_context(query, k=5):
    query_embedding = embed_model.encode([query])
    distances, indices = index.search(np.array(query_embedding), k)
    relevant_chunks = [texts[i] for i in indices[0] if i < len(texts)]
    return "\n".join(relevant_chunks)

def chat_fn(message, history):
    # Search knowledge base
    context = search_context(message)

    if not context.strip():
        return "❌ Sorry, I can only answer questions related to the content of logiqcurve.com."

    # Construct prompt
    messages = [
        {"role": "system", "content": "You are an assistant that only answers based on the provided CONTEXT. Do not answer from general knowledge. If the answer is not in the CONTEXT, reply with 'Sorry, I can only answer questions related to the content of logiqcurve.com.'"},
        {"role": "user", "content": f"CONTEXT:\n{context}\n\nQUESTION:\n{message}"},
    ]

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

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

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

    return reply

# Gradio Chat Interface (for Hugging Face Space)
chatbot = gr.ChatInterface(
    fn=chat_fn,
    title="LogiqCurve ChatBot",
    description="Ask anything about logiqcurve.com. This bot only answers based on its content.",
    theme="soft"
)

chatbot.launch()