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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()
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