app.py

import json
import joblib
import pandas as pd
import gradio as gr
import spaces
from huggingface_hub import hf_hub_download

REPO_ID = "Swetha1929/predictive-maintenance-engine-model"

model_path = hf_hub_download(repo_id=REPO_ID, filename="best_model.pkl")
feature_path = hf_hub_download(repo_id=REPO_ID, filename="feature_names.txt")

model = joblib.load(model_path)

with open(feature_path, "r", encoding="utf-8") as f:
    feature_names = [line.strip() for line in f if line.strip()]

try:
    info_path = hf_hub_download(repo_id=REPO_ID, filename="model_info.json")
    with open(info_path, "r", encoding="utf-8") as f:
        model_info = json.load(f)
except Exception:
    model_info = {}

@spaces.GPU
def predict(*values):
    input_df = pd.DataFrame([list(values)], columns=feature_names)
    prediction = model.predict(input_df)[0]

    if hasattr(model, "predict_proba"):
        probs = model.predict_proba(input_df)[0]
        return str(prediction), {f"Class {i}": float(p) for i, p in enumerate(probs)}

    return str(prediction), {}

inputs = [gr.Number(label=feature) for feature in feature_names]

demo = gr.Interface(
    fn=predict,
    inputs=inputs,
    outputs=[
        gr.Textbox(label="Predicted Engine Condition"),
        gr.Label(label="Prediction Probabilities")
    ],
    title="Engine Condition Prediction",
    description="Enter engine parameters to predict the engine condition."
)

demo.launch()