Delete app.py

app.py

@@ -1,45 +0,0 @@
-# (nra_upd_02)
-import streamlit as st
-import pandas as pd
-from huggingface_hub import hf_hub_download
-import joblib
-from config import HF_REPO_ID     #this doesnt work and the reference is not found on HF run
-
-
-# HF_REPO_ID = "Nra/Machine-Failure-Prediction" # name is case sensitive
-
-# Download and load the model
-model_path = hf_hub_download(repo_id=HF_REPO_ID, filename="best_machine_failure_model_v1.joblib") # repo_id is case-sensitive
-model = joblib.load(model_path)
-
-# Streamlit UI for Machine Failure Prediction
-st.title("Machine Failure Prediction App")
-st.write("""
-This application predicts the likelihood of a machine failing based on its operational parameters.
-Please enter the sensor and configuration data below to get a prediction.
-""")
-
-# User input
-Type = st.selectbox("Machine Type", ["H", "L", "M"])
-air_temp = st.number_input("Air Temperature (K)", min_value=250.0, max_value=400.0, value=298.0, step=0.1)
-process_temp = st.number_input("Process Temperature (K)", min_value=250.0, max_value=500.0, value=324.0, step=0.1)
-rot_speed = st.number_input("Rotational Speed (RPM)", min_value=0, max_value=3000, value=1400)
-torque = st.number_input("Torque (Nm)", min_value=0.0, max_value=100.0, value=40.0, step=0.1)
-tool_wear = st.number_input("Tool Wear (min)", min_value=0, max_value=300, value=10)
-
-# Assemble input into DataFrame
-input_data = pd.DataFrame([{
-    'Air temperature': air_temp,
-    'Process temperature': process_temp,
-    'Rotational speed': rot_speed,
-    'Torque': torque,
-    'Tool wear': tool_wear,
-    'Type': Type
-}])
-
-
-if st.button("Predict Failure"):
-    prediction = model.predict(input_data)[0]
-    result = "Machine Failure" if prediction == 1 else "No Failure"
-    st.subheader("Prediction Result:")
-    st.success(f"The model predicts: **{result}**")