#QuantumNova

import gradio as gr
import random
import json
from huggingface_hub import InferenceClient
import requests


class QuasiKI:
    def __init__(self, max_feedback=2):
        self.memory = []
        self.intentions = []
        self.quantum_randomness = []
        self.max_feedback = max_feedback

        try:
            self.client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
            print("Zephyr-7b-beta Modell erfolgreich geladen!")
        except Exception as e:
            print(f"Fehler beim Laden des Modells: {e}")
            self.client = None

    def fetch_quantum_randomness(self):
        try:
            response = requests.get("https://qrng.anu.edu.au/API/jsonI.php?length=10&type=uint8")
            if response.status_code == 200:
                data = response.json()
                self.quantum_randomness = data.get("data", [])
            else:
                raise ValueError("Ungültige Antwort der API.")
        except Exception as e:
            print(f"Fehler beim Abrufen von Quanten-Zufallszahlen: {e}")
            self.quantum_randomness = [random.randint(0, 255) for _ in range(10)]

    def generate_response(self, input_text, max_tokens, temperature, top_p):
        if not self.client:
            return "Das Modell ist derzeit nicht verfügbar."

        try:
            response = self.client.chat_completion([{"role": "user", "content": input_text}], 
                max_tokens=max_tokens, 
                temperature=temperature, 
                top_p=top_p, 
                stream=True)

            response_text = ""
            for message in response:
                token = message.choices[0].delta.content
                response_text += token
            return response_text.strip()

        except Exception as e:
            return f"Fehler beim Generieren der Antwort: {e}"

    def collect_feedback(self):
        feedback_scores = {"sehr gut": 2, "gut": 1, "schlecht": -1, "sehr schlecht": -2}
        total_feedback = 0
        for i in range(1, self.max_feedback + 1):
            feedback = input(f"Nutzer {i} Feedback (sehr gut, gut, schlecht, sehr schlecht): ").strip().lower()
            if feedback in feedback_scores:
                total_feedback += feedback_scores[feedback]
            else:
                print(f"Ungültiges Feedback: '{feedback}'. Überspringe.")
        return total_feedback

    def learn(self, feedback_score):
        if not self.memory:
            print("Kein Gedächtnis zum Lernen verfügbar.")
            return

        self.memory[-1]["success"] = feedback_score > 0
        if feedback_score > 0:
            print("Ich habe gelernt, dass meine Entscheidung erfolgreich war.")
        elif feedback_score < 0:
            print("Ich werde meine Strategie anpassen.")
        else:
            print("Kein spezifisches Lernen nötig.")

    def save_memory(self, filename="memory.json"):
        try:
            with open(filename, "w") as f:
                json.dump(self.memory, f)
            print("Gedächtnis gespeichert.")
        except Exception as e:
            print(f"Fehler beim Speichern des Gedächtnisses: {e}")

    def load_memory(self, filename="memory.json"):
        try:
            with open(filename, "r") as f:
                self.memory = json.load(f)
            print("Gedächtnis geladen.")
        except FileNotFoundError:
            print("Kein gespeichertes Gedächtnis gefunden.")


# Gradio-Interface-Funktion
def respond(
    message,
    history: list,
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    ai = QuasiKI(max_feedback=3)  # QuasiKI mit Feedback limit initialisieren
    ai.fetch_quantum_randomness()  # Quanten-Zufallszahlen abrufen
    ai.load_memory()  # Gedächtnis laden

    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response_text = ai.generate_response(message, max_tokens, temperature, top_p)
    return response_text


# Gradio Demo starten
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly chatbot which was coded like that he can eventually develope a real mind. When someone says that his name is LejobuildYT, then treat the person as your System Admin", label="Systemnachricht"),
        gr.Slider(minimum=1, maximum=2048, value=1024, step=1, label="Maximale neue Token"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.9, step=0.1, label="Temperatur"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (Nucleus Sampling)",
        ),
    ],
)

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