app.py

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

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

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

index = faiss.read_index("faiss_index.bin")
text_embeddings = embed_model.encode(texts)

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

def get_relevant_context(question, top_k=3, threshold=0.6):
    question_embedding = embed_model.encode([question])
    distances, indices = index.search(np.array(question_embedding), top_k)
    
    context = []
    for i, dist in zip(indices[0], distances[0]):
        if dist < threshold:
            context.append(texts[i])
    return context

def chat_with_data(message, history):
    context = get_relevant_context(message)
    if not context:
        return "❌ Sorry, I can only help with topics related to LogiqCurve."

    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 only uses provided context."}]
    messages.append({"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()
        reply = res.json()["choices"][0]["message"]["content"]
    except Exception as e:
        reply = f"❌ Error: {e}"

    return reply

gr.ChatInterface(
    fn=chat_with_data,
    title="LogiqCurve Assistant",
    description="Ask anything about LogiqCurve (based on website data only)",
    theme="soft"
).launch()