app.py

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."
    )
    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 temperature on sports performance.")

# Input for temperature
temperature = st.number_input("Temperature (°C):", min_value=-50, max_value=50, value=25)

# Button to generate predictions
if st.button("Generate Prediction"):
    conditions = {
        "temperature": temperature
    }

    try:
        with st.spinner("Generating predictions..."):
            # Call AI model to get qualitative analysis
            qualitative_analysis = (
                f"Assess the impact on sports performance at temperature: "
                f"{temperature}°C."
            )
            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 Temperature")

        # Plot performance score against temperature
        fig, ax = plt.subplots()
        ax.plot(conditions['temperature'], performance_scores, marker='o', linestyle='-', color='b')
        ax.set_xlabel('Temperature (°C)')
        ax.set_ylabel('Performance Score')
        ax.set_title('Performance Score vs Temperature')
        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}")