Create app.py

app.py

@@ -0,0 +1,94 @@
+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