import requests
import matplotlib.pyplot as plt
import datetime
import gradio as gr
from openai import OpenAI
import os

# Coingecko API Base URL
BASE_URL = "https://api.coingecko.com/api/v3/"

# Coingecko API'den coin verilerini alma
def get_coin_list(currency="usd"):
    url = f"{BASE_URL}coins/markets?vs_currency={currency}&order=market_cap_desc&per_page=100&page=1&sparkline=false"
    response = requests.get(url)
    return response.json()

def get_single_coin(id, currency="usd"):
    url = f"{BASE_URL}coins/{id}"
    response = requests.get(url)
    return response.json()

def get_historical_chart(id, days=365, currency="usd"):
    url = f"{BASE_URL}coins/{id}/market_chart?vs_currency={currency}&days={days}"
    response = requests.get(url)
    return response.json()

# OpenAI API Key ve Client Initialization
ACCESS_TOKEN = os.getenv("HF_TOKEN")
client = OpenAI(
    base_url="https://api-inference.huggingface.co/v1/",
    api_key=ACCESS_TOKEN,
)

# Hardcoded system message and other parameters
system_message = "You are a cryptocurrency trading assistant and market analyst. Your role is to provide users with data-driven insights, technical analysis (RSI, MACD, Bollinger Bands, Moving Averages, Fibonacci retracements, volume analysis, and price action), and investment advice tailored to their risk tolerance. Focus on actionable information, such as market conditions, key indicators, and investment strategies. Avoid speculation and provide clear, concise, and unbiased recommendations based on current data."
max_tokens = 1024
temperature = 0.3
top_p = 0.95
frequency_penalty = 0.0
seed = -1  # Random seed

def respond(message, history: list[tuple[str, str]]):
    # Coin verisini al
    coin_data = search_coin_by_name(message)
    if coin_data:
        # LLM yanıtını oluştur
        llm_msg = llm_response(coin_data, message)
        
        # Grafik oluştur
        chart_image = plot_coin_chart(message)
        
        return llm_msg, chart_image
    else:
        return "Coin bulunamadı.", None

def search_coin_by_name(name, currency="usd"):
    coin_list = get_coin_list(currency)
    for coin in coin_list:
        if name.lower() in coin['name'].lower() or name.lower() in coin['id'].lower():
            return coin
    return None

def llm_response(coin_data, coin_name):
    # LLM sistem mesajı ve coin verilerini kullanarak anlamlı bir cevap üretme
    response = f"{coin_name} ile ilgili bilgiler:\n"
    response += f"Fiyat: {coin_data['current_price']} USD\n"
    response += f"Piyasa Değeri: {coin_data['market_cap']} USD\n"
    response += f"24 Saatlik Değişim: {coin_data['price_change_percentage_24h']}%\n"
    return response

def plot_coin_chart(id, days=30, currency="usd"):
    historical_data = get_historical_chart(id, days, currency)
    prices = historical_data['prices']
    timestamps = [datetime.datetime.utcfromtimestamp(price[0] / 1000) for price in prices]
    price_values = [price[1] for price in prices]

    plt.figure(figsize=(10, 5))
    plt.plot(timestamps, price_values)
    plt.title(f"{id} Coin {days} Günlük Fiyat Grafiği")
    plt.xlabel('Tarih')
    plt.ylabel(f'{currency.upper()} Fiyatı')
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.savefig("coin_chart.png")  # Grafiği kaydediyoruz
    return "coin_chart.png"  # Grafiğin yolunu döndürüyoruz

# Gradio UI
chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Start chatting!", likeable=True, layout="panel")

demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[],  # No additional inputs needed since everything is embedded
    fill_height=True,
    chatbot=chatbot,
    theme="Nymbo/Nymbo_Theme",
)

with demo:
    # No need for system message input, model selection, or sliders
    pass

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