Upload folder using huggingface_hub

Dockerfile

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+FROM python:3.10
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY . .
+
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+st.title("Engine Fault Prediction")
+
+# download model
+model_path = hf_hub_download(
+    repo_id="SunnyShaurya/engine-fault-model",
+    filename="best_model.pkl"
+)
+
+model = joblib.load(model_path)
+
+st.header("Enter Engine Sensor Values")
+
+rpm = st.number_input("Engine RPM")
+oil_p = st.number_input("Lub Oil Pressure")
+fuel_p = st.number_input("Fuel Pressure")
+cool_p = st.number_input("Coolant Pressure")
+oil_t = st.number_input("Lub Oil Temperature")
+cool_t = st.number_input("Coolant Temperature")
+
+if st.button("Predict"):
+    data = pd.DataFrame([{
+        "Engine_RPM": rpm,
+        "Lub_Oil_Pressure": oil_p,
+        "Fuel_Pressure": fuel_p,
+        "Coolant_Pressure": cool_p,
+        "Lub_Oil_Temperature": oil_t,
+        "Coolant_Temperature": cool_t
+    }])
+
+    result = model.predict(data)[0]
+
+    if result == 1:
+        st.error("⚠ Engine Fault Detected")
+    else:
+        st.success("✅ Engine Normal")

requirements.txt

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+streamlit
+pandas
+scikit-learn
+joblib
+huggingface_hub