Upload folder using huggingface_hub

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+# System dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    git \
+ && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py .
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

app.py

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+
+import os
+import joblib
+import pandas as pd
+import gradio as gr
+from huggingface_hub import hf_hub_download
+
+# 1) SET YOUR MODEL REPO HERE (Model Hub repo, not dataset repo)
+MODEL_REPO = os.getenv("MODEL_REPO", "SabarnaDeb/Capstone_PredictiveMaintenance_Model")
+MODEL_FILE = os.getenv("MODEL_FILE", "model.joblib") 
+
+# 2) Download model file from Hugging Face Model Hub
+model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE, repo_type="model")
+model = joblib.load(model_path)
+
+# 3) Feature list must match your training columns
+FEATURES = [
+    "engine_rpm",
+    "lub_oil_pressure",
+    "fuel_pressure",
+    "coolant_pressure",
+    "lub_oil_temperature",
+    "coolant_temperature"
+]
+
+def predict(engine_rpm, lub_oil_pressure, fuel_pressure, coolant_pressure,
+            lub_oil_temperature, coolant_temperature):
+
+    # 4) Save inputs into a DataFrame
+    data = {
+        "engine_rpm": [engine_rpm],
+        "lub_oil_pressure": [lub_oil_pressure],
+        "fuel_pressure": [fuel_pressure],
+        "coolant_pressure": [coolant_pressure],
+        "lub_oil_temperature": [lub_oil_temperature],
+        "coolant_temperature": [coolant_temperature],
+    }
+    input_df = pd.DataFrame(data)
+
+    # 5) Predict
+    pred = model.predict(input_df[FEATURES])[0]
+
+    prob = None
+    if hasattr(model, "predict_proba"):
+        prob = float(model.predict_proba(input_df[FEATURES])[:, 1][0])
+
+    # 6) Business-friendly output
+    if int(pred) == 1:
+        msg = "⚠️ Maintenance Needed"
+    else:
+        msg = "✅ Normal Operation"
+
+    if prob is not None:
+        msg += f"\nConfidence (maintenance probability): {prob:.2f}"
+
+    return msg, input_df
+
+demo = gr.Interface(
+    fn=predict,
+    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 Temperature"),
+        gr.Number(label="Coolant Temperature"),
+    ],
+    outputs=[
+        gr.Textbox(label="Prediction Result"),
+        gr.Dataframe(label="Input Data (saved as DataFrame)")
+    ],
+    title="Predictive Maintenance – Engine Health",
+    description="Enter engine sensor readings to predict whether maintenance is needed."
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

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+gradio
+pandas
+scikit-learn
+joblib
+huggingface_hub