app.py

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

# Function to call the Together API with the provided 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("Impact of Climate on Sports Using AI")
st.write("Predict and mitigate the impacts of climate change on sports performance and infrastructure.")

# Climate data inputs
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)

# Athlete-specific inputs
age = st.number_input("Athlete Age:", min_value=0, max_value=100, value=25)
sport = st.selectbox("Select Sport:", ["Running", "Cycling", "Swimming", "Football", "Basketball"])
performance_history = st.text_area("Athlete Performance History:")

# Infrastructure characteristics
facility_type = st.selectbox("Facility Type:", ["Stadium", "Gymnasium", "Outdoor Field"])
facility_age = st.number_input("Facility Age (years):", min_value=0, max_value=100, value=10)
materials_used = st.text_input("Materials Used in Construction:")

# Socio-economic data
community_size = st.number_input("Community Size:", min_value=0, value=1000)
economic_impact_estimate = st.text_area("Estimate Economic Impact (Event cancellations, Facility damage costs):")

if st.button("Generate Prediction"):
    all_message = (
        f"Given the climate conditions: Temperature {temperature}°C, Humidity {humidity}%, Wind Speed {wind_speed} km/h, "
        f"UV Index {uv_index}, Air Quality Index {air_quality_index}, Precipitation {precipitation} mm, "
        f"Atmospheric Pressure {atmospheric_pressure} hPa. For athlete (Age: {age}, Sport: {sport}), "
        f"Facility (Type: {facility_type}, Age: {facility_age}, Materials: {materials_used}). "
        f"Assess the impact on sports performance, infrastructure, and socio-economic aspects."
    )

    try:
        with st.spinner("Generating response..."):
            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

            st.success("Response generated!")

            # Display the impact summary and conclusions
            st.markdown(f"**Impact Summary:** {generated_text.strip()}")
            st.markdown("**Conclusion:** Tailoring strategies based on these climate conditions can significantly enhance performance and infrastructure resilience.")

            # Data Visualization
            st.subheader("Climate Condition Impacts Visualization")
            # Example: Displaying data in a table
            data = {
                'Condition': ['Temperature', 'Humidity', 'Wind Speed', 'UV Index', 'Air Quality Index', 'Precipitation', 'Atmospheric Pressure'],
                'Value': [temperature, humidity, wind_speed, uv_index, air_quality_index, precipitation, atmospheric_pressure]
            }
            df = pd.DataFrame(data)
            st.table(df)

            # Plotting a bar chart for climate variables
            fig, ax = plt.subplots()
            ax.bar(data['Condition'], data['Value'], color=['blue', 'green', 'orange', 'red', 'purple', 'gray', 'cyan'])
            ax.set_ylabel('Value')
            ax.set_title('Climate Condition Impacts')
            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}")