import streamlit as st
import requests
import os
import json
import pandas as pd
import matplotlib.pyplot as plt

# 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

# Function to get performance data from AI
def get_performance_data(conditions):
    all_message = (
        f"Provide the expected sports performance score at conditions: "
        f"Temperature {conditions['temperature']}°C, Humidity {conditions['humidity']}%, "
        f"Wind Speed {conditions['wind_speed']} km/h, UV Index {conditions['uv_index']}, "
        f"Air Quality Index {conditions['air_quality_index']}, Precipitation {conditions['precipitation']} mm, "
        f"Atmospheric Pressure {conditions['atmospheric_pressure']} hPa."
    )
    response = call_ai_model(all_message)
    generated_text = ""
    for line in response.iter_lines():
        if line:
            line_content = line.decode('utf-8')
            if line_content.startswith("data: "):
                line_content = line_content[6:]  # Strip "data: " prefix
            try:
                json_data = json.loads(line_content)
                if "choices" in json_data:
                    delta = json_data["choices"][0]["delta"]
                    if "content" in delta:
                        generated_text += delta["content"]
            except json.JSONDecodeError:
                continue

    # Example: Replace with actual data from API
    performance_scores = [75, 80, 70, 85, 78, 72, 82]  # Replace with actual data from API
    return performance_scores

# 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"):
    conditions = {
        "temperature": temperature,
        "humidity": humidity,
        "wind_speed": wind_speed,
        "uv_index": uv_index,
        "air_quality_index": air_quality_index,
        "precipitation": precipitation,
        "atmospheric_pressure": atmospheric_pressure
    }

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

            # Get performance score for specified conditions
            performance_scores = get_performance_data(conditions)

        st.success("Predictions generated.")

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

        # Display performance score
        st.subheader("Performance Score")
        st.write(f"Predicted Performance Scores: {performance_scores}")

        # Plotting the data
        st.subheader("Performance Score vs Climate Conditions")

        # Define climate conditions for plotting
        climate_conditions = list(conditions.keys())
        climate_values = list(conditions.values())

        # Plotting performance score against climate conditions
        fig, ax = plt.subplots()
        ax.plot(climate_conditions, performance_scores, marker='o', linestyle='-', color='b')
        ax.set_xlabel('Climate Conditions')
        ax.set_ylabel('Performance Score')
        ax.set_title('Performance Score vs Climate Conditions')
        ax.grid(True)
        st.pyplot(fig)

    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}")