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

# ✅ Load context data
with open("texts.json", "r", encoding="utf-8") as f:
    texts = json.load(f)

index = faiss.read_index("faiss_index.bin")
embed_model = SentenceTransformer("all-MiniLM-L6-v2")

API_KEY = os.environ.get("OPENROUTER_API_KEY")
MODEL = "qwen/qwen-2.5-coder-32b-instruct:free"

# ✅ Get top-k context from FAISS
def get_context(query, top_k=5):
    query_vec = embed_model.encode([query])
    D, I = index.search(np.array(query_vec), top_k)
    return "\n".join([texts[i] for i in I[0]])

# ✅ Gradio chatbot function (takes message and history)
def chat_fn(message, history):
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }

    context = get_context(message)
    messages = [
        {"role": "system", "content": "You are a helpful assistant. Use the following context: " + context},
        {"role": "user", "content": message}
    ]

    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

# ✅ ChatInterface now exposed at /chat
demo = gr.ChatInterface(
    fn=chat_fn,
    title="CODEX MIRXA KAMRAN",
    description="Chat with our RAG-enabled coding assistant powered by OpenRouter",
    examples=["How do I use pandas to filter rows?", "Fix this bug in my code", "What is Python typing?"],
    theme="soft",
    chatbot=gr.Chatbot(height=400),
    api_name="//chat"  # ✅ enable POST access via /chat
)

if __name__ == "__main__":
    demo.launch()