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import streamlit as st
import requests
import os
import json
import pandas as pd
import matplotlib.pyplot as plt
# 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.")
# Input fields for user to enter data
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)
if st.button("Generate Prediction"):
all_message = (
f"Predict the impact on sports performance and infrastructure given the following climate conditions: "
f"Temperature {temperature}°C, Humidity {humidity}%, Wind Speed {wind_speed} km/h, UV Index {uv_index}."
)
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
# Display concise response and conclusion
st.success("Response generated!")
# Constructing the summary and conclusion
summary = f"**Impact Summary:** {generated_text.strip()}\n"
conclusion = "**Conclusion:** Proper adaptation to these climate conditions is essential for maintaining sports performance and infrastructure resilience."
# Display text
st.markdown(summary)
st.markdown(conclusion)
# Example data for charts
data = {
'Condition': ['Temperature', 'Humidity', 'Wind Speed', 'UV Index'],
'Value': [temperature, humidity, wind_speed, uv_index]
}
df = pd.DataFrame(data)
# Displaying a table
st.table(df)
# Plotting a bar chart
fig, ax = plt.subplots()
ax.bar(data['Condition'], data['Value'], color=['blue', 'green', 'orange', 'red'])
ax.set_ylabel('Value')
ax.set_title('Climate Condition Impact Indicators')
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}")
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