import streamlit as st
import requests
import datetime
import os
from supabase import create_client, Client
from dotenv import load_dotenv


# Load environment variables
load_dotenv()

# Get API keys from environment variables
WEATHER_API_KEY = os.getenv("OPENWEATHERMAP_API_KEY")
HF_API_KEY = os.getenv("HUGGINGFACE_API_KEY",)
SUPABASE_URL = os.getenv("SUPABASE_URL")
SUPABASE_KEY =  os.getenv("SUPABASE_KEY")

# Initialize the Hugging Face Inference Client



# Initialize Supabase
supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)


def call_llvm_model(prompt):
    llvm_model_url = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3/v1/chat/completions"
    payload = {
    "model": "mistralai/Mistral-7B-Instruct-v0.3",
    "messages": [
        {
        "role": "user",
        "content": prompt,
        }
    ],
    "max_tokens": 500,
    "stream": False
    }
    headers = {
    "Authorization": f"Bearer {HF_API_KEY}",
    "content-type": "application/json"
    }

    response = requests.post(llvm_model_url, json=payload, headers=headers)

    response = response.json()
    return response['choices'][0]['message']['content']

def generate_outfit_image(clothing_suggestion):
    prompt = f"A fashion illustration showing an outfit with {clothing_suggestion}. Stylized, colorful, no text."
    
    payload = {
        "inputs": prompt,
        "negative_prompt":"blurry, low quality, text, words, labels"
    }

    API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1"
    headers = {"Authorization": f"Bearer {HF_API_KEY}"}

    response = requests.post(API_URL, headers=headers, json=payload)
    return response.content

def get_weather(city):
    base_url = "http://api.openweathermap.org/data/2.5/weather"
    params = {
        "q": city,
        "appid": WEATHER_API_KEY,
        "units": "metric"  # For Celsius
    }
    response = requests.get(base_url, params=params)
    return response.json()

def get_ai_clothing_suggestion(weather_data):
    prompt = f"""
    Given the following weather conditions:
    Temperature: {weather_data['main']['temp']}°C
    Weather: {weather_data['weather'][0]['main']} ({weather_data['weather'][0]['description']})
    Humidity: {weather_data['main']['humidity']}%
    Wind Speed: {weather_data['wind']['speed']} m/s

    Suggest appropriate clothing to wear, including top, bottom.
    Make sure to stick to hugging faces free response size limit.
    """

    return call_llvm_model(prompt)

def get_ai_weather_explanation(weather_data):
    prompt = f"""
    Given the following weather conditions:
    Temperature: {weather_data['main']['temp']}°C
    Weather: {weather_data['weather'][0]['main']} ({weather_data['weather'][0]['description']})
    Humidity: {weather_data['main']['humidity']}%
    Wind Speed: {weather_data['wind']['speed']} m/s

    Give me the description of the weather.
    Make sure to stick to hugging faces free response size limit.
    """

    return call_llvm_model(prompt)

def get_relevant_quote(weather_condition):

    url = "https://api-inference.huggingface.co/models/mixedbread-ai/mxbai-embed-large-v1"

    payload = { "inputs": weather_condition}
    headers = {
    "content-type": "application/json",
    "Authorization": f"Bearer {HF_API_KEY}"
    }

    response = requests.post(url, json=payload, headers=headers)

    weather_embedding = response.json()

    response =  supabase.rpc("match_quote_embeddings",{
            'query_embedding': weather_embedding,
            'match_threshold': 0.5,
            'match_count': 5
    }).execute()


    if response.data and len(response.data) > 0:
        return response.data[0]['content']
    else:
        return "No relevant quote found."

st.title("AI-Powered Weather and Clothing Suggestion App")

city = st.text_input("Enter a city name:", "London")

if st.button("Get Weather and Clothing Suggestion"):
    weather_data = get_weather(city)
    
    if weather_data["cod"] != "404":
        main_weather = weather_data["weather"][0]["main"]
        description = weather_data["weather"][0]["description"]
        temperature = weather_data["main"]["temp"]
        humidity = weather_data["main"]["humidity"]
        wind_speed = weather_data["wind"]["speed"]
        
        st.subheader(f"Weather in {city}:")
        st.write(f"Condition: {main_weather} ({description})")
        st.write(f"Temperature: {temperature:.1f}°C")
        st.write(f"Humidity: {humidity}%")
        st.write(f"Wind Speed: {wind_speed} m/s")
        
        with st.spinner("Generating clothing suggestion..."):
            clothing_suggestion = get_ai_clothing_suggestion(weather_data)
        st.subheader("What to Wear (AI Suggestion):")
        st.write(clothing_suggestion)
        with st.spinner("Finding a relevant quote..."):
            weather_description = get_ai_weather_explanation(weather_data)
            quote = get_relevant_quote(f"{weather_description}")

        st.subheader("Quote of the Day:")
        st.write(quote)
        st.subheader("Weather description:")
        st.write(weather_description)
    else:
        st.error("City not found. Please check the spelling and try again.")

    with st.spinner("Generating outfit image..."):
        outfit_image = generate_outfit_image(clothing_suggestion)
    st.subheader("Outfit Visualization:")
    st.image(outfit_image, caption="AI-generated outfit based on the suggestion")

# Display current date and time
st.sidebar.write(f"Current Date and Time: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")