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from flask import Flask, render_template, request, jsonify
from llm.agents import query_moa_chain
import os
import dotenv
import asyncio
import json
# Load secrets from .env
dotenv.load_dotenv()
app = Flask(__name__)
@app.route("/")
def index():
return render_template("index.html")
@app.route("/docs")
def docs():
return render_template("docs.html")
# === New models endpoint ===
@app.route("/models", methods=["GET"])
def get_models():
try:
with open("llm/model_config.json", "r") as f:
config = json.load(f)
models = [{"id": model_id, "name": model_id.split(":")[0].split("/")[-1].replace("-", " ").title()} for model_id in config["models"].keys()]
return jsonify(models)
except Exception as e:
return jsonify({"error": str(e)}), 500
@app.route("/chat", methods=["POST"])
def chat():
data = request.get_json()
user_input = data.get("prompt", "")
settings = data.get("settings", {})
if not user_input:
return jsonify({"error": "Empty prompt."}), 400
try:
# Fully async call to query MoA chain
final_response = asyncio.run(query_moa_chain(user_input, settings))
return jsonify({"response": final_response})
except Exception as e:
return jsonify({"error": str(e)}), 500
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860, debug=False) # Hugging Face uses port 7860
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