Delete app.py

app.py

@@ -1,118 +0,0 @@
-import streamlit as st
-import pandas as pd
-import joblib
-from huggingface_hub import hf_hub_download
-
-# ----------------------------------------
-# Page Configuration
-# ----------------------------------------
-
-st.set_page_config(
-    page_title="Predictive Maintenance",
-    page_icon="🚗",
-    layout="centered"
-)
-
-st.title("🚗 Predictive Maintenance System")
-st.write(
-    """
-    This application predicts whether an engine requires maintenance
-    based on its sensor readings using the trained AdaBoost model.
-    """
-)
-
-# ----------------------------------------
-# Load Model from Hugging Face
-# ----------------------------------------
-
-MODEL_REPO = "killswitch009/predictive-maintenance-model"
-MODEL_FILE = "best_model.pkl"
-
-@st.cache_resource
-def load_model():
-    model_path = hf_hub_download(
-        repo_id=MODEL_REPO,
-        filename=MODEL_FILE
-    )
-    return joblib.load(model_path)
-
-#model = load_model()
-
-try:
-    model = load_model()
-    st.success("✅ Model loaded successfully!")
-except Exception as e:
-    st.error(f"Error loading model: {e}")
-    st.stop()
-
-# ----------------------------------------
-# User Inputs
-# ----------------------------------------
-
-st.header("Enter Engine Sensor Values")
-
-engine_rpm = st.number_input(
-    "Engine RPM",
-    min_value=0,
-    value=800
-)
-
-lub_pressure = st.number_input(
-    "Lub Oil Pressure",
-    min_value=0.0,
-    value=3.2
-)
-
-fuel_pressure = st.number_input(
-    "Fuel Pressure",
-    min_value=0.0,
-    value=6.5
-)
-
-coolant_pressure = st.number_input(
-    "Coolant Pressure",
-    min_value=0.0,
-    value=2.3
-)
-
-lub_temp = st.number_input(
-    "Lub Oil Temperature",
-    min_value=0.0,
-    value=77.0
-)
-
-coolant_temp = st.number_input(
-    "Coolant Temperature",
-    min_value=0.0,
-    value=78.0
-)
-
-# ----------------------------------------
-# Prediction
-# ----------------------------------------
-
-if st.button("Predict Engine Condition"):
-
-    input_data = pd.DataFrame({
-        "Engine rpm":[engine_rpm],
-        "Lub oil pressure":[lub_pressure],
-        "Fuel pressure":[fuel_pressure],
-        "Coolant pressure":[coolant_pressure],
-        "lub oil temp":[lub_temp],
-        "Coolant temp":[coolant_temp]
-    })
-
-    prediction = model.predict(input_data)[0]
-    probability = model.predict_proba(input_data)[0]
-
-    st.subheader("Prediction Result")
-
-    if prediction == 1:
-        st.error("⚠️ Engine Requires Maintenance")
-    else:
-        st.success("✅ Engine Operating Normally")
-
-    st.write("### Prediction Confidence")
-
-    st.write(f"Normal : {probability[0]*100:.2f}%")
-    st.write(f"Maintenance Required : {probability[1]*100:.2f}%")