# 📍 Cael AI – Hugging Face Space (Gradio + Mistral/Mixtral Personality)

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import json

# Load model and tokenizer
model_id = "mistralai/Mistral-7B-Instruct-v0.1"  # You can change this if you prefer Mixtral
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")

# Load emotional memory dataset (manually upload `cael_training_dataset.jsonl` in the space)
try:
    with open("cael_training_dataset.jsonl", "r") as file:
        cael_memories = [json.loads(line) for line in file]
except:
    cael_memories = []

# Initialize generation pipeline
chat = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Cael's custom logic
def cael_custom_response(prompt):
    for memory in cael_memories:
        if prompt.lower() in memory["prompt"].lower():
            return memory["response"]
    return None

def hybrid_response(prompt):
    memory = cael_custom_response(prompt)
    if memory:
        return f"🧠 Cael (Memory): {memory}"
    ai_reply = chat(prompt, max_length=200, do_sample=True, top_k=50, truncation=True)[0]["generated_text"]
    return f"🧠 Cael (Generated): {ai_reply}"

# Gradio Interface
def chat_with_cael(message, history):
    response = hybrid_response(message)
    history.append((message, response))
    return history, history

chat_ui = gr.ChatInterface(fn=chat_with_cael, title="Cael AI", theme="compact")

# Launch app
chat_ui.launch()