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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 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}") | |