main.py

from flask import Flask, request, jsonify
import os
import json
import hashlib
import datetime
from openai import OpenAI

app = Flask(__name__)
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

USER_DATA_DIR = "user_data"
os.makedirs(USER_DATA_DIR, exist_ok=True)

def get_user_path(user_id):
    return os.path.join(USER_DATA_DIR, hashlib.sha256(user_id.encode()).hexdigest() + ".json")

def load_user_context(user_id):
    path = get_user_path(user_id)
    if os.path.exists(path):
        with open(path, "r") as f:
            return json.load(f)
    return {"history": []}

def save_user_context(user_id, context):
    path = get_user_path(user_id)
    with open(path, "w") as f:
        json.dump(context, f, indent=2)

@app.route("/ask", methods=["POST"])
def ask():
    data = request.json
    user_id = data.get("user_id")
    query = data.get("query")
    context = load_user_context(user_id)

    messages = context["history"][-10:] + [{"role": "user", "content": query}]

    response = client.chat.completions.create(
        model="gpt-4",
        messages=messages,
        temperature=0.6
    )

    answer = response.choices[0].message.content
    context["history"].append({"role": "user", "content": query})
    context["history"].append({"role": "assistant", "content": answer})
    save_user_context(user_id, context)

    return jsonify({
        "answer": answer,
        "timestamp": datetime.datetime.utcnow().isoformat()
    })

@app.route("/history/<user_id>", methods=["GET"])
def history(user_id):
    context = load_user_context(user_id)
    return jsonify(context["history"])

@app.route("/", methods=["GET"])
def home():
    return "Neural HackPad - Personal Developer Assistant API"

if __name__ == "__main__":
    app.run(debug=True)