Delete app.py

app.py

@@ -1,113 +0,0 @@
-import streamlit as st
-import pandas as pd
-import joblib
-import numpy as np
-import plotly.graph_objects as go
-import os
-import xgboost as xgb
-
-# 1. PAGE SETUP
-st.set_page_config(page_title="Jet Engine AI Predictor", page_icon="✈️", layout="wide")
-
-# 2. AUTO-TRAINING LOGIC (Fixes the "Missing File" or "Corrupted File" issue)
-def train_and_save_model():
-    with st.spinner("Training the AI model for the first time... please wait."):
-        # Load Data from NASA Repository
-        url = "https://raw.githubusercontent.com/datasets-machine-learning/nasa-turbofan-failure-prediction/master/data/train_FD001.txt"
-        cols = ['unit', 'cycles', 'os1', 'os2', 'os3'] + [f's{i}' for i in range(1, 22)]
-        df = pd.read_csv(url, sep='\s+', header=None, names=cols)
-
-        # Calculate RUL (Target)
-        max_cycles = df.groupby('unit')['cycles'].max().reset_index()
-        max_cycles.columns = ['unit', 'max_of_unit']
-        df = df.merge(max_cycles, on='unit', how='left')
-        df['RUL'] = df['max_of_unit'] - df['cycles']
-
-        # Select Features (15 columns)
-        features = ['cycles', 's2', 's3', 's4', 's7', 's8', 's11', 's12', 's13', 's15', 's17', 's20', 's21', 'os1', 'os2']
-        X = df[features]
-        y = df['RUL']
-
-        # Train XGBoost
-        model = xgb.XGBRegressor(n_estimators=100, learning_rate=0.1, max_depth=5)
-        model.fit(X, y)
-        
-        # Save locally in the HF Space
-        joblib.dump(model, 'engine_model.pkl')
-        return model
-
-@st.cache_resource
-def load_model():
-    if os.path.exists('engine_model.pkl'):
-        try:
-            return joblib.load('engine_model.pkl')
-        except:
-            # If the file is corrupted (like the mport error), re-train it
-            return train_and_save_model()
-    else:
-        return train_and_save_model()
-
-# Load model on startup
-model = load_model()
-
-# 3. UI LAYOUT
-st.title("✈️ Jet Engine Predictive Maintenance System")
-st.markdown("---")
-
-col1, col2 = st.columns([1, 2])
-
-with col1:
-    st.header("📥 Sensor Inputs")
-    st.write("Adjust sliders to simulate engine conditions:")
-    
-    # Inputs
-    cycle = st.slider("Current Cycles", 1, 350, 100)
-    s2 = st.slider("Temp (S2)", 640.0, 650.0, 642.5)
-    s3 = st.slider("Temp (S3)", 1580.0, 1600.0, 1589.0)
-    s4 = st.slider("Pressure (S4)", 1400.0, 1430.0, 1408.0)
-    s7 = st.slider("Pressure (S7)", 550.0, 560.0, 553.5)
-    s11 = st.slider("Speed (S11)", 47.0, 48.5, 47.5)
-    
-    # Analyze Button
-    predict_btn = st.button("Predict Engine Health", type="primary")
-
-with col2:
-    st.header("📊 Prediction Result")
-    
-    if predict_btn:
-        # Prepare inputs (must be 15 values to match model training)
-        # Using some static defaults for the other 9 features to keep UI simple
-        sample_input = np.array([[cycle, s2, s3, s4, s7, 550, s11, 2388, 521, 8.4, 392, 39, 23, 0.001, 0.0002]])
-        
-        # Make Prediction
-        prediction = model.predict(sample_input)
-        rul = int(max(0, prediction[0]))
-
-        # Gauge Chart
-        fig = go.Figure(go.Indicator(
-            mode = "gauge+number",
-            value = rul,
-            title = {'text': "Remaining Useful Life (Cycles)"},
-            gauge = {
-                'axis': {'range': [0, 200]},
-                'steps' : [
-                    {'range': [0, 30], 'color': "red"},
-                    {'range': [30, 80], 'color': "orange"},
-                    {'range': [80, 200], 'color': "green"}],
-                'bar': {'color': "black"}
-            }
-        ))
-        st.plotly_chart(fig)
-
-        # Status Message
-        if rul < 30:
-            st.error(f"🚨 CRITICAL: Engine failure likely in {rul} cycles!")
-        elif rul < 80:
-            st.warning(f"⚠️ CAUTION: Maintenance check required within {rul} cycles.")
-        else:
-            st.success(f"✅ HEALTHY: Engine is safe for {rul} more cycles.")
-    else:
-        st.info("Adjust the sliders and click 'Predict' to see the results.")
-
-st.markdown("---")
-st.caption("B.Tech AI&DS Project | Dataset: NASA C-MAPSS")