import os
import openai
import json
import requests
import streamlit as st
#from dotenv import load_dotenv

# Set API keys
openai.api_key = os.environ['OPENAI_API_KEY']
OPENWEATHER_API_KEY = os.environ['OPENWEATHER_API_KEY']

# Function to get the current weather
def get_current_weather(location):
    """Get the current weather in a given location"""
    base_url = "http://api.openweathermap.org/data/2.5/weather?"
    complete_url = f"{base_url}appid={OPENWEATHER_API_KEY}&q={location}"

    response = requests.get(complete_url)

    if response.status_code == 200:
        data = response.json()
        weather = data['weather'][0]['main']
        temperature = data['main']['temp'] - 273.15  # Convert Kelvin to Celsius
        return {
            "city": location,
            "weather": weather,
            "temperature": round(temperature, 2)
        }
    else:
        return {"city": location, "weather": "Data Fetch Error", "temperature": "N/A"}

# Streamlit app
st.title("Weather Info Assistant")
#st.write("Get the current weather information for a specific location. Just replace Gurgaon with the location")

user_input = st.text_input("Get the current weather information for a specific location. Just replace Gurgaon with the location", "What is the weather like in Gurgaon?")

if st.button("Get Weather"):
    # Use GPT-3.5-turbo to parse the user query
    response = openai.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {
                "role": "user",
                "content": user_input
            }
        ],
        temperature=0,
        max_tokens=300,
        functions=[
            {
                "name": "get_current_weather",
                "description": "Get the current weather in a given location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                    },
                    "required": ["location"],
                },
            }
        ],
        function_call="auto"
    )

    # Extract the location from the response
    try:
        # Extract the function call information
        function_call_info = response.choices[0].message.function_call
        function_name = function_call_info.name  # This should be 'get_current_weather'
        function_arguments = function_call_info.arguments  # This is a JSON string

        # Parse the JSON string to get the arguments dictionary
        args = json.loads(function_arguments)
        location = args['location']

        # Now you can fetch the weather information using the extracted location
        weather_info = get_current_weather(location)

        # Define a dictionary to map weather conditions to emoticons
        weather_icons = {
            "Clear": "☀️",  # Sun icon
            "Clouds": "☁️",  # Cloud icon
            "Rain": "🌧️",  # Rain icon
            "Snow": "❄️",  # Snow icon
            "Thunderstorm": "⛈️",  # Thunderstorm icon
            "Mist": "🌫️",  # Fog icon
            "Smoke": "💨",  # Smoke icon
            "Haze": "🌫️",  # Haze icon
            "Dust": "🌪️",  # Dust icon
            "Fog": "🌫️",  # Fog icon
            "Sand": "🏜️",  # Sand icon
            "Ash": "🌋",  # Volcanic ash icon
            "Squall": "🌬️",  # Wind face icon
            "Tornado": "🌪️"  # Tornado icon
        }

        if weather_info["weather"] != "Data Fetch Error":
            icon = weather_icons.get(weather_info["weather"], "❓")  # Default to question mark if no match
            weather_description = f"The weather in **{weather_info['city']}** is currently `{weather_info['weather']}` with a temperature of `{weather_info['temperature']}°C`."
            chat_response = openai.chat.completions.create(
                model="gpt-3.5-turbo",
                messages=[
                    {
                        "role": "system",
                        "content": "Generate a conversational response for weather information. Please include the weather and temperature details in the response and provide suggestion on things that can be done best in this weather"
                    },
                    {
                        "role": "user",
                        "content": weather_description
                    }
                ],
                temperature=0.5,
                max_tokens=300
            )
                        
            # Use HTML to style the font size, color, and include the icon in the displayed message
            st.markdown(f"<div style='font-size: 24px; color: blue; font-weight: bold;'> Weather: {weather_info['weather']} {icon} &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp; Temperature: {weather_info['temperature']}°C</div>", unsafe_allow_html=True)
            st.markdown(f"<div style='font-size: 20px;'> {chat_response.choices[0].message.content}", unsafe_allow_html=True)
        else:
            st.markdown(f"<div style='font-size: 20px;'>**Could not fetch weather data for {weather_info['city']}. Please try again.**</div>", unsafe_allow_html=True)
    except Exception as e:
        print(f"Error processing the response or fetching the weather: {e}")