Update app.py

app.py

@@ -1,65 +1,95 @@
 import streamlit as st
-from transformers import pipeline
+import pandas as pd
 import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
 from groq import Groq  # Assuming this is the correct import
+import requests
 
 # Initialize Groq API
 GROQ_API_KEY = "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"
 client = Groq(api_key=GROQ_API_KEY)
 
-# Mock Resource Monitoring System
-RESOURCE_LIMITS = {
-    "Oxygen Level": (80, 100),
-    "Food Reserves (days)": (10, 365),
-    "Power Availability (%)": (30, 100),
-    "Communication Signals (Strength)": (50, 100),
-}
+st.title("Space Mission Analysis Dashboard")
 
-def fetch_health_data():
-    """Simulate fetching health data for astronauts."""
-    return {
-        "Heart Rate (bpm)": np.random.randint(50, 120),
-        "Oxygen Saturation (%)": np.random.randint(80, 100),
-        "Stress Indicator": np.random.choice(["Low", "Moderate", "High"])
-    }
-
-def fetch_resource_data():
-    """Simulate fetching resource data."""
-    return {key: np.random.uniform(*limits) for key, limits in RESOURCE_LIMITS.items()}
+# Load dataset
+data = pd.DataFrame({
+    "Mission ID": [],
+    "Mission Name": [],
+    "Launch Date": [],
+    "Target Type": [],
+    "Target Name": [],
+    "Mission Type": [],
+    "Distance from Earth (light-years)": [],
+    "Mission Duration (years)": [],
+    "Mission Cost (billion USD)": [],
+    "Scientific Yield (points)": [],
+    "Crew Size": [],
+    "Mission Success (%)": [],
+    "Fuel Consumption (tons)": [],
+    "Payload Weight (tons)": [],
+    "Launch Vehicle": []
+})
 
-def groq_predict_alert(message):
-    """Use Groq API to analyze emergency alerts."""
-    try:
-        result = client.predict({"text": message})
-        return result.get("label", "No label detected")
-    except Exception as e:
-        return f"Error using Groq API: {e}"
+# Upload dataset
+uploaded_file = st.file_uploader("Upload your dataset (CSV format)", type=["csv"])
+if uploaded_file:
+    data = pd.read_csv(uploaded_file)
+    st.write("### Dataset Preview")
+    st.write(data.head())
 
-# Streamlit UI
-st.title("ARMS: Autonomous Resource Management System")
-st.write("Track, analyze, and optimize survival resources for space missions.")
+# Display basic statistics
+if not data.empty:
+    st.write("### Dataset Summary")
+    st.write(data.describe())
 
-st.subheader("Real-Time Monitoring")
-resource_data = fetch_resource_data()
-health_data = fetch_health_data()
+    # Visualization: Mission Success vs. Scientific Yield
+    st.write("### Mission Success vs. Scientific Yield")
+    plt.figure(figsize=(10, 5))
+    sns.scatterplot(
+        data=data,
+        x="Mission Success (%)",
+        y="Scientific Yield (points)",
+        hue="Mission Type",
+        palette="viridis"
+    )
+    plt.title("Success Rate vs Yield")
+    st.pyplot(plt)
 
-col1, col2 = st.columns(2)
-with col1:
-    st.write("### Resource Metrics")
-    for key, value in resource_data.items():
-        st.metric(key, f"{value:.2f}")
+    # Filterable Data Selection
+    st.write("### Filtered Data Selection")
+    mission_types = data["Mission Type"].unique()
+    selected_type = st.selectbox("Select Mission Type", mission_types)
+    filtered_data = data[data["Mission Type"] == selected_type]
+    st.write(filtered_data)
 
-with col2:
-    st.write("### Astronaut Health Metrics")
-    for key, value in health_data.items():
-        st.metric(key, value)
+    # Groq API Call Example
+    st.write("### AI Prediction with Groq API")
+    sample_input = {
+        "mission_duration": filtered_data["Mission Duration (years)"].mean(),
+        "fuel_consumption": filtered_data["Fuel Consumption (tons)"].mean(),
+        "success_rate": filtered_data["Mission Success (%)"].mean(),
+    }
+    
+    if st.button("Predict Resource Optimization"):
+        response = requests.post(
+            "https://api.groq.com/v1/predict",  # Replace with actual endpoint
+            headers={"Authorization": f"Bearer {GROQ_API_KEY}"},
+            json=sample_input
+        )
+        if response.status_code == 200:
+            prediction = response.json()
+            st.write("### AI Prediction Result")
+            st.json(prediction)
+        else:
+            st.error("Error: Could not get prediction")
 
-# Predictive Analytics
-st.subheader("Predictive Analysis & Alerts")
-if st.button("Run Groq Analysis"):
-    alert_message = "Critical alert: Resources dangerously low. Immediate action required!"
-    groq_result = groq_predict_alert(alert_message)
-    st.write("Groq API Result:", groq_result)
-    st.warning(alert_message)
+    # Insights: Fuel Efficiency Analysis
+    st.write("### Fuel Efficiency Analysis")
+    data["Fuel Efficiency (Yield per Ton)"] = (
+        data["Scientific Yield (points)"] / data["Fuel Consumption (tons)"]
+    ).replace([np.inf, -np.inf], np.nan).dropna()
+    st.line_chart(data["Fuel Efficiency (Yield per Ton)"])
 
-st.write("Developed for Hackathon using Groq API, Streamlit, and Hugging Face.")
+else:
+    st.warning("Please upload a valid CSV dataset.")