import os
from supabase import create_client
import requests, tempfile
import gradio as gr
from llama_cpp import Llama

#MODEL DOWNLOAD & LOAD
MODEL_URL = "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf"
#MODEL_URL = "https://huggingface.co/TheBloke/bling-stable-lm-3b-4e1t-v0-GGUF/resolve/main/bling-stable-lm-3b-4e1t-v0.Q4_K_M.gguf"

print("Downloading model into temporary memory... This may take a while ⏳")
response = requests.get(MODEL_URL, stream=True)
response.raise_for_status()

#Save to a temporary file (deleted when app ends)
with tempfile.NamedTemporaryFile(delete=False, suffix=".gguf") as tmp_file:
    for chunk in response.iter_content(chunk_size=8192):
        tmp_file.write(chunk)
    temp_model_path = tmp_file.name

#Load model
llm = Llama(
    model_path=temp_model_path,
    n_ctx=512,   # smaller context
    n_threads=2  # adjust based on your CPU
)

#SUPABASE SETUP
SUPABASE_URL = os.getenv("SUPABASE_URL")
SUPABASE_KEY = os.getenv("SUPABASE_KEY")

supabase = create_client(SUPABASE_URL, SUPABASE_KEY)

def save_to_supabase(user_id, prompt, response, model, tokens=None):
    try:
        supabase.table("ai_logs").insert({
            "user_id": user_id,      
            "prompt": prompt,       
            "response": response,           
            "model": model,         
            "tokens": tokens          
        }).execute()
    except Exception as e:
        print("Supabase insert failed:", e)

#CHAT FUNCTION WITH ROLLING CONTEXT
MAX_HISTORY = 4  # Keep last 2 user+assistant exchanges for stability

def chat_with_infinite_agent(message, lang, history):
    history = history or []

    #Keep only the last MAX_HISTORY items
    history = history[-MAX_HISTORY:]

    #Build context from previous messages (trimmed for stability)
    context_lines = []
    for m, a in zip(history[::2], history[1::2]):
        if m['role']=='user' and a['role']=='assistant':
            #Trim very long messages to reduce memory
            user_msg = m['content'][:300]
            assistant_msg = a['content'][:300]
            context_lines.append(f"User: {user_msg}\nAgent: {assistant_msg}")
    context = "\n".join(context_lines)

    #Language adjustment
    if lang == "Svenska":
        message = f"Svara på svenska: {message}"
    elif lang == "Türkçe":
        message = f"Türkçe cevapla: {message}"

    #Full prompt with your Human Design & Gene Keys
    prompt = f"""
    You are Infinite Agent — the AI embodiment of Tugce Ozdeger,
    a 5/1 Emotional Manifestor whose wisdom flows through Gene Keys:
    12, 22, 11, 37, 21, 61, 31, 39, 46, and 25.
    Guide others toward emotional clarity, self-worth, and life direction
    with empathy, depth, and grace.
    {context}
    User: {message}
    Agent:
    """

    #Generate response safely
    try:
        response = llm(prompt, max_tokens=512)
        output = response["choices"][0]["text"].strip()
    except Exception as e:
        print("LLM error:", e)
        output = "⚠️ Sorry, the model failed to respond. Please try again."

    #Append new messages to history
    history.append({"role": "user", "content": message})
    history.append({"role": "assistant", "content": output})

    save_to_supabase(
    user_id="anonymous",       
    prompt=message,           
    response=output,                            
    model=MODEL_URL,           
    tokens=len(message)
)
    return history, history

#GRADIO UI WITH DARK MODE
css_path = os.path.join(os.path.dirname(__file__), "style.css")

with gr.Blocks(title="Infinite Agent", css=open(css_path).read()) as demo:
    #Avatar image
    gr.Image("avatar.png", elem_id="agent-avatar")  # No shape styling, just default

    #Header
    gr.Markdown(
        "### 🌙 Infinite Agent — Emotional Clarity & Life Direction\n"
        "_Guided by Tugce Ozdeger’s Human Design & Gene Keys_",
        elem_classes="header-text"
    )

    #Language selector
    language = gr.Dropdown(["English", "Svenska", "Türkçe"], label="Choose language", value="English")

    #Chatbot with welcome message
    chatbot = gr.Chatbot(type="messages", value=[
        {"role": "system", "content": "🌙 Welcome to Infinite Agent — your guide to emotional clarity, self-worth, and life direction."}
    ])

    #Input textbox and clear button
    msg = gr.Textbox(placeholder="Ask about your emotions, direction, or purpose...")
    clear = gr.Button("Clear")

    #Submit & clear actions
    msg.submit(chat_with_infinite_agent, [msg, language, chatbot], [chatbot, chatbot])
    clear.click(lambda: [], None, chatbot, queue=False)  # clears chat

demo.launch()