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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}") | |