app.py

import streamlit as st
import requests
import os
import json
import pandas as pd
import plotly.graph_objects as go
import time

# Function to call the Together AI model for the initial analysis
def call_ai_model_initial(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 call the Together AI model for analyzing the text and computing performance score
def call_ai_model_analysis(analysis_text):
    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": analysis_text}],
        "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 and Infrastructure in Kenya")
st.write("Analyze and visualize the impact of climate conditions on sports performance and infrastructure, with a focus on Kenya.")

# 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)

# Kenya-specific inputs
region = st.text_input("Enter region in Kenya:")
elevation = st.number_input("Elevation (m):", min_value=0, max_value=5000, value=1000)

# Sports and athlete inputs
sports = st.multiselect("Select sports:", ["Athletics", "Football", "Rugby", "Volleyball", "Boxing", "Swimming"])
athlete_types = st.multiselect("Select athlete types:", ["Professional", "Amateur", "Youth", "Senior"])

# Infrastructure inputs
infrastructure_types = st.multiselect("Select infrastructure types:", ["Outdoor Stadium", "Indoor Arena", "Training Facility", "Community Sports Ground"])

if st.button("Generate Prediction and Analysis"):
    all_message = (
        f"Assess the impact on sports performance, athletes, and infrastructure in Kenya based on climate conditions: "
        f"Temperature {temperature}°C, Humidity {humidity}%, Wind Speed {wind_speed} km/h, UV Index {uv_index}, "
        f"Air Quality Index {air_quality_index}, Precipitation {precipitation} mm, Atmospheric Pressure {atmospheric_pressure} hPa. "
        f"Region: {region}, Elevation: {elevation}m. "
        f"Sports: {', '.join(sports)}. Athlete types: {', '.join(athlete_types)}. "
        f"Infrastructure types: {', '.join(infrastructure_types)}. "
        f"Provide a detailed analysis of how these conditions affect performance, health, and infrastructure in Kenya. "
        f"Include specific impacts for each sport, athlete type, and infrastructure type. "
        f"Also, provide an overall performance score and an infrastructure impact score, both as percentages. "
        f"Suggest mitigation strategies for both performance and infrastructure. "
        f"Assess the socio-economic implications of these climate impacts on sports in Kenya, including equitable access to sports facilities. "
        f"Organize the information in tables with the following columns: Climate Conditions, Impact on Sports Performance, "
        f"Impact on Athletes' Health, Impact on Infrastructure, Mitigation Strategies, Socio-Economic Implications. "
        f"Be as accurate and specific to Kenya as possible in your analysis. And please do not generate long texts, make it as short and precise as possible, i am stressing on this please, generate something short."
    )

    try:
        stages = [
            "Analyzing climate conditions...",
            "Checking location data...",
            "Fetching historical data...",
            "Running simulations...",
            "Processing current weather...",
            "Assessing environmental factors...",
            "Calculating predictions...",
            "Compiling results...",
            "Finalizing analysis...",
            "Preparing output..."
        ]
        
        with st.spinner("Analyzing climate conditions and generating predictions..."):
            # Loop through each stage, updating the spinner text and waiting for 2 seconds
            for stage in stages:
                st.spinner(stage)
                time.sleep(2)
            
            initial_response = call_ai_model_initial(all_message)
    
            initial_text = ""
            for line in initial_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:
                                initial_text += delta["content"]
                    except json.JSONDecodeError:
                        continue
    
            st.success("Analysis completed!")
            # Display prediction
            st.subheader("Climate Impact Analysis for Sports in Kenya")
            st.markdown(initial_text.strip())

            # Extract and display scores
            performance_score = "N/A"
            infrastructure_score = "N/A"
            for line in initial_text.split('\n'):
                if "performance score:" in line.lower():
                    performance_score = line.split(":")[-1].strip()
                elif "infrastructure impact score:" in line.lower():
                    infrastructure_score = line.split(":")[-1].strip()

            # Display performance and infrastructure scores
            col1, col2 = st.columns(2)
            with col1:
                st.metric("Overall Performance Score", performance_score)
            with col2:
                st.metric("Infrastructure Impact Score", infrastructure_score)

            # Prepare data for visualization
            results_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]
            }
            results_df = pd.DataFrame(results_data)

            # Display results in a table
            st.subheader("Climate Conditions Summary")
            st.table(results_df)

            # Create a radar chart for climate conditions
            fig = go.Figure(data=go.Scatterpolar(
                r=[temperature/50*100, humidity, wind_speed/2, uv_index/11*100, air_quality_index/5, precipitation/5, (atmospheric_pressure-900)/2],
                theta=results_df['Condition'],
                fill='toself'
            ))
            fig.update_layout(
                polar=dict(
                    radialaxis=dict(visible=True, range=[0, 100])
                ),
                showlegend=False
            )
            st.plotly_chart(fig)

            # Display analyzed sports and infrastructure
            st.subheader("Analyzed Components")
            col1, col2, col3 = st.columns(3)
            with col1:
                st.write("**Sports:**")
                for sport in sports:
                    st.write(f"- {sport}")
            with col2:
                st.write("**Athlete Types:**")
                for athlete_type in athlete_types:
                    st.write(f"- {athlete_type}")
            with col3:
                st.write("**Infrastructure Types:**")
                for infra_type in infrastructure_types:
                    st.write(f"- {infra_type}")

            # Socio-economic impact analysis
            st.subheader("Socio-Economic Impact Analysis")
            socio_economic_prompt = (
                f"Based on the climate conditions and sports analysis for {region}, Kenya, "
                f"provide a brief assessment of the socio-economic implications, including impacts on: "
                f"1) Local economy, 2) Community health, 3) Sports tourism, 4) Equitable access to sports facilities. "
                f"Consider the specific context of Kenya and the selected region. and make the response very precise and short, do not yap"
            )
        
            with st.spinner("Analyzing socio-economic impacts..."):
                socio_economic_response = call_ai_model_analysis(socio_economic_prompt)
                socio_economic_text = ""
                for line in socio_economic_response.iter_lines():
                    if line:
                        line_content = line.decode('utf-8')
                        if line_content.startswith("data: "):
                            line_content = line_content[6:]
                        try:
                            json_data = json.loads(line_content)
                            if "choices" in json_data:
                                delta = json_data["choices"][0]["delta"]
                                if "content" in delta:
                                    socio_economic_text += delta["content"]
                        except json.JSONDecodeError:
                            continue
        
            st.markdown(socio_economic_text.strip())

            # Mitigation strategies
            st.subheader("Mitigation Strategies")
            mitigation_prompt = (
                f"Based on the climate conditions and sports analysis for {region}, Kenya, "
                f"suggest specific mitigation strategies for: "
                f"1) Improving athlete performance and health, 2) Enhancing infrastructure resilience, "
                f"3) Ensuring equitable access to sports facilities. "
                f"Consider the specific context of Kenya and the selected region. And make the response very precise and short, do not yap"
            )
        
            with st.spinner("Generating mitigation strategies..."):
                mitigation_response = call_ai_model_analysis(mitigation_prompt)
                mitigation_text = ""
                for line in mitigation_response.iter_lines():
                    if line:
                        line_content = line.decode('utf-8')
                        if line_content.startswith("data: "):
                            line_content = line_content[6:]
                        try:
                            json_data = json.loads(line_content)
                            if "choices" in json_data:
                                delta = json_data["choices"][0]["delta"]
                                if "content" in delta:
                                    mitigation_text += delta["content"]
                        except json.JSONDecodeError:
                            continue
        
            st.markdown(mitigation_text.strip())

            # Display raw analysis result for debugging
            with st.expander("Show Raw Analysis"):
                st.text(initial_text)

    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}")