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import streamlit as st |
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import requests |
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import os |
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import json |
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import pandas as pd |
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import time |
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import matplotlib.pyplot as plt |
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def call_ai_model(all_message): |
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url = "https://api.together.xyz/v1/chat/completions" |
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payload = { |
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"model": "NousResearch/Nous-Hermes-2-Yi-34B", |
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"temperature": 1.05, |
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"top_p": 0.9, |
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"top_k": 50, |
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"repetition_penalty": 1, |
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"n": 1, |
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"messages": [{"role": "user", "content": all_message}], |
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"stream_tokens": True, |
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} |
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TOGETHER_API_KEY = os.getenv('TOGETHER_API_KEY') |
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if TOGETHER_API_KEY is None: |
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raise ValueError("TOGETHER_API_KEY environment variable not set.") |
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headers = { |
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"accept": "application/json", |
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"content-type": "application/json", |
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"Authorization": f"Bearer {TOGETHER_API_KEY}", |
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} |
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response = requests.post(url, json=payload, headers=headers, stream=True) |
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response.raise_for_status() |
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return response |
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def get_numeric_performance_data(temperature): |
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all_message = ( |
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f"Provide the expected numeric sports performance value (as a score) at a temperature of {temperature}°C." |
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) |
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response = call_ai_model(all_message) |
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generated_text = "" |
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for line in response.iter_lines(): |
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if line: |
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line_content = line.decode('utf-8') |
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if line_content.startswith("data: "): |
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line_content = line_content[6:] |
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try: |
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json_data = json.loads(line_content) |
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if "choices" in json_data: |
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delta = json_data["choices"][0]["delta"] |
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if "content" in delta and delta["content"].strip().replace('.', '', 1).isdigit(): |
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return float(delta["content"].strip()) |
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except json.JSONDecodeError: |
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continue |
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return None |
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st.title("Climate Impact on Sports Performance and Infrastructure") |
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st.write("Analyze and visualize the impact of climate conditions on sports performance and infrastructure.") |
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temperature = st.number_input("Temperature (°C):", min_value=-50, max_value=50, value=25) |
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humidity = st.number_input("Humidity (%):", min_value=0, max_value=100, value=50) |
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wind_speed = st.number_input("Wind Speed (km/h):", min_value=0.0, max_value=200.0, value=15.0) |
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uv_index = st.number_input("UV Index:", min_value=0, max_value=11, value=5) |
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air_quality_index = st.number_input("Air Quality Index:", min_value=0, max_value=500, value=100) |
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precipitation = st.number_input("Precipitation (mm):", min_value=0.0, max_value=500.0, value=10.0) |
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atmospheric_pressure = st.number_input("Atmospheric Pressure (hPa):", min_value=900, max_value=1100, value=1013) |
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if st.button("Generate Prediction"): |
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all_message = ( |
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f"Assess the impact on sports performance and infrastructure based on climate conditions: " |
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f"Temperature {temperature}°C, Humidity {humidity}%, Wind Speed {wind_speed} km/h, UV Index {uv_index}, " |
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f"Air Quality Index {air_quality_index}, Precipitation {precipitation} mm, Atmospheric Pressure {atmospheric_pressure} hPa." |
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) |
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try: |
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with st.spinner("Analyzing climate conditions..."): |
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response = call_ai_model(all_message) |
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st.success("Initial analysis complete. Generating detailed predictions...") |
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generated_text = "" |
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for line in response.iter_lines(): |
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if line: |
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line_content = line.decode('utf-8') |
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if line_content.startswith("data: "): |
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line_content = line_content[6:] |
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try: |
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json_data = json.loads(line_content) |
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if "choices" in json_data: |
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delta = json_data["choices"][0]["delta"] |
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if "content" in delta and delta["content"].strip().replace('.', '', 1).isdigit(): |
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generated_text += delta["content"] |
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except json.JSONDecodeError: |
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continue |
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st.success("Detailed predictions generated.") |
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results_data = { |
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"Condition": ["Temperature", "Humidity", "Wind Speed", "UV Index", "Air Quality Index", "Precipitation", "Atmospheric Pressure"], |
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"Value": [temperature, humidity, wind_speed, uv_index, air_quality_index, precipitation, atmospheric_pressure] |
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} |
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results_df = pd.DataFrame(results_data) |
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st.subheader("Results Summary") |
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st.table(results_df) |
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st.markdown("**Predicted Impact on Performance and Infrastructure:**") |
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st.markdown(generated_text.strip()) |
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st.success("Generating performance data...") |
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temperatures = range(-10, 41, 5) |
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performance_values = [] |
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for temp in temperatures: |
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st.spinner(f"Fetching performance data for {temp}°C...") |
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performance_value = get_numeric_performance_data(temp) |
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if performance_value is not None: |
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performance_values.append(performance_value) |
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time.sleep(1) |
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if performance_values: |
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fig, ax = plt.subplots() |
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ax.plot(temperatures, performance_values, marker='o') |
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ax.set_xlabel('Temperature (°C)') |
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ax.set_ylabel('Performance Score') |
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ax.set_title('Temperature vs. Numeric Sports Performance') |
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st.pyplot(fig) |
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except ValueError as ve: |
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st.error(f"Configuration error: {ve}") |
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except requests.exceptions.RequestException as re: |
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st.error(f"Request error: {re}") |
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except Exception as e: |
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st.error(f"An unexpected error occurred: {e}") |
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