import streamlit as st
import requests
import os
import json

# Function to call the Together AI model
def call_ai_model(all_message):
    url = "https://api.together.xyz/v1/chat/completions"
    payload = {
        "model": "NousResearch/Nous-Hermes-2-Yi-34B",
        "temperature": 1.05,
        "top_p": 0.9,
        "top_k": 50,
        "repetition_penalty": 1,
        "n": 1,
        "messages": [{"role": "user", "content": all_message}],
        "stream_tokens": True,
    }

    TOGETHER_API_KEY = os.getenv('TOGETHER_API_KEY')
    if TOGETHER_API_KEY is None:
        raise ValueError("TOGETHER_API_KEY environment variable not set.")
    
    headers = {
        "accept": "application/json",
        "content-type": "application/json",
        "Authorization": f"Bearer {TOGETHER_API_KEY}",
    }

    response = requests.post(url, json=payload, headers=headers, stream=True)
    response.raise_for_status()  # Ensure HTTP request was successful

    return response

# Streamlit app layout
st.title("Climate Impact on Sports Performance")
st.write("Analyze and visualize the impact of climate conditions on sports performance.")

# Inputs for climate conditions
temperature = st.number_input("Temperature (°C):", min_value=-50, max_value=50, value=25)
humidity = st.number_input("Humidity (%):", min_value=0, max_value=100, value=50)
wind_speed = st.number_input("Wind Speed (km/h):", min_value=0.0, max_value=200.0, value=15.0)
uv_index = st.number_input("UV Index:", min_value=0, max_value=11, value=5)
air_quality_index = st.number_input("Air Quality Index:", min_value=0, max_value=500, value=100)
precipitation = st.number_input("Precipitation (mm):", min_value=0.0, max_value=500.0, value=10.0)
atmospheric_pressure = st.number_input("Atmospheric Pressure (hPa):", min_value=900, max_value=1100, value=1013)

# Button to generate predictions
if st.button("Generate Prediction"):
    all_message = (
        f"Assess the impact on sports performance based on climate conditions: "
        f"Temperature {temperature}°C, Humidity {humidity}%, Wind Speed {wind_speed} km/h, "
        f"UV Index {uv_index}, Air Quality Index {air_quality_index}, "
        f"Precipitation {precipitation} mm, Atmospheric Pressure {atmospheric_pressure} hPa."
    )

    try:
        with st.spinner("Generating predictions..."):
            # Call AI model to get qualitative analysis
            qualitative_analysis = (
                f"Analyze the impact on sports performance under the following conditions: "
                f"Temperature {temperature}°C, Humidity {humidity}%, Wind Speed {wind_speed} km/h, "
                f"UV Index {uv_index}, Air Quality Index {air_quality_index}, "
                f"Precipitation {precipitation} mm, Atmospheric Pressure {atmospheric_pressure} hPa."
            )
            qualitative_result = call_ai_model(qualitative_analysis)

        st.success("Predictions generated.")

        # Display qualitative analysis
        st.subheader("Qualitative Analysis")
        st.write(qualitative_result)

    except ValueError as ve:
        st.error(f"Configuration error: {ve}")
    except requests.exceptions.RequestException as re:
        st.error(f"Request error: {re}")
    except Exception as e:
        st.error(f"An unexpected error occurred: {e}")