Upload folder using huggingface_hub

.ipynb_checkpoints/app-checkpoint.py

@@ -0,0 +1,40 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the model
+model_path = hf_hub_download(repo_id="SSudhar/Machine_Failure_Model", filename="best_machine_failure_model_v1.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI for Machine Failure Prediction
+st.title("Machine Failure Prediction App - Demo")
+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}**")

app.py

@@ -8,7 +8,7 @@ model_path = hf_hub_download(repo_id="SSudhar/Machine_Failure_Model", filename="
 model = joblib.load(model_path)
 
 # Streamlit UI for Machine Failure Prediction
-st.title("Machine Failure Prediction App")
+st.title("Machine Failure Prediction App - Demo")
 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.