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import os
from groq import Groq
# Set API Key (Replace with your actual API key)
os.environ["GROQ_API_KEY"] = "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"
# Initialize the Groq Client
client = Groq(api_key=os.environ["GROQ_API_KEY"])
# Test Query
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": "Explain the importance of fast language models"}],
model="llama-3.3-70b-versatile",
)
# Print response
print(chat_completion.choices[0].message.content)
import random
import time
import pandas as pd
# Function to simulate data updates
def get_real_time_data():
return {
"Heart Rate (BPM)": random.randint(60, 120),
"Oxygen Saturation (%)": round(random.uniform(85, 100), 1),
"Blood Pressure (mmHg)": f"{random.randint(90, 120)}/{random.randint(60, 80)}",
"Respiratory Rate (BPM)": random.randint(12, 20),
"Hydration Level (%)": round(random.uniform(40, 100), 1),
"Battery Level (%)": random.randint(10, 100),
"Food Supply (Days)": random.randint(1, 10),
"Water Supply (Liters)": random.randint(1, 50),
}
# Testing the function
for _ in range(5):
print(get_real_time_data())
time.sleep(1)
def predict_survival_time(data):
oxygen_factor = data["Oxygen Saturation (%)"] / 100
hydration_factor = data["Hydration Level (%)"] / 100
battery_factor = data["Battery Level (%)"] / 100
food_factor = data["Food Supply (Days)"] / 10
survival_hours = (oxygen_factor + hydration_factor + battery_factor + food_factor) * 10
return round(survival_hours, 2)
# Example test
sample_data = get_real_time_data()
print("Predicted Survival Time (Hours):", predict_survival_time(sample_data))
import streamlit as st
import pandas as pd
import random
import time
st.title("🚀 Astronaut Survival Monitor")
# Simulating real-time data updates
def get_real_time_data():
return {
"Heart Rate (BPM)": random.randint(60, 120),
"Oxygen Saturation (%)": round(random.uniform(85, 100), 1),
"Blood Pressure (mmHg)": f"{random.randint(90, 120)}/{random.randint(60, 80)}",
"Respiratory Rate (BPM)": random.randint(12, 20),
"Hydration Level (%)": round(random.uniform(40, 100), 1),
"Battery Level (%)": random.randint(10, 100),
"Food Supply (Days)": random.randint(1, 10),
"Water Supply (Liters)": random.randint(1, 50),
}
# Survival Time Prediction
def predict_survival_time(data):
oxygen_factor = data["Oxygen Saturation (%)"] / 100
hydration_factor = data["Hydration Level (%)"] / 100
battery_factor = data["Battery Level (%)"] / 100
food_factor = data["Food Supply (Days)"] / 10
survival_hours = (oxygen_factor + hydration_factor + battery_factor + food_factor) * 10
return round(survival_hours, 2)
# Real-time simulation
data = get_real_time_data()
survival_time = predict_survival_time(data)
st.metric("Predicted Survival Time", f"{survival_time} Hours")
# Display real-time health and resource data
st.write("### Health Metrics")
for key, value in data.items():
st.metric(key, value)
st.warning("🚨 Alert: Low Oxygen or Power Levels Detected!") if data["Oxygen Saturation (%)"] < 90 or data["Battery Level (%)"] < 20 else None
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