Spaces:
Sleeping
Sleeping
File size: 2,219 Bytes
4fd6e54 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
import gradio as gr
import joblib
import numpy as np
import pandas as pd
from huggingface_hub import hf_hub_download
# Download model & scaler from Hugging Face Hub
model_path = hf_hub_download(repo_id="thalaivanan/pm-decision-tree", filename="model.joblib")
scaler_path = hf_hub_download(repo_id="thalaivanan/pm-decision-tree", filename="scaler.joblib")
model = joblib.load(model_path)
scaler = joblib.load(scaler_path)
feature_names = ["engine_rpm", "lub_oil_pressure", "fuel_pressure", "coolant_pressure", "lub_oil_temp", "coolant_temp"]
# Single prediction function
def predict_single(engine_rpm, lub_oil_pressure, fuel_pressure, coolant_pressure, lub_oil_temp, coolant_temp):
features = np.array([[engine_rpm, lub_oil_pressure, fuel_pressure, coolant_pressure, lub_oil_temp, coolant_temp]])
scaled_features = scaler.transform(features)
prediction = model.predict(scaled_features)[0]
return "Faulty" if prediction == 1 else "Active"
# Batch prediction function
def predict_batch(file):
df = pd.read_csv(file)
if not all(col in df.columns for col in feature_names):
return "Error: CSV must contain columns: " + ", ".join(feature_names)
scaled_features = scaler.transform(df[feature_names])
predictions = model.predict(scaled_features)
df["prediction"] = ["Faulty" if p == 1 else "Active" for p in predictions]
output_file = "predictions.csv"
df.to_csv(output_file, index=False)
return output_file
# Gradio UI
single_inputs = [
gr.Number(label="Engine RPM"),
gr.Number(label="Lub Oil Pressure"),
gr.Number(label="Fuel Pressure"),
gr.Number(label="Coolant Pressure"),
gr.Number(label="Lub Oil Temp"),
gr.Number(label="Coolant Temp")
]
single_output = gr.Textbox(label="Engine Condition")
batch_input = gr.File(label="Upload CSV", file_types=[".csv"])
batch_output = gr.File(label="Download Predictions")
tab1 = gr.Interface(fn=predict_single, inputs=single_inputs, outputs=single_output, title="Single Prediction")
tab2 = gr.Interface(fn=predict_batch, inputs=batch_input, outputs=batch_output, title="Batch Prediction")
demo = gr.TabbedInterface([tab1, tab2], ["Single Prediction", "Batch Prediction"])
demo.launch(share=True)
|