Add Gradio app with batch prediction

README.md

@@ -1,13 +1,2 @@
----
-title: Predictive Maintenance
-emoji: 📈
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
-pinned: false
-short_description: Predictive Maintenance App
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Predictive Maintenance App
+This app predicts engine condition (Faulty or Active) based on sensor readings using a Decision Tree model.

app.py

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+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)

requirements.txt

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+streamlit
+scikit-learn
+imbalanced-learn
+xgboost
+joblib
+huggingface_hub
+numpy
+matplotlib