Upload folder using huggingface_hub

Dockerfile

@@ -7,6 +7,7 @@ COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
 COPY app.py .
+COPY best_model.joblib .
 
 EXPOSE 7860
 

app.py

@@ -1,20 +1,13 @@
+
 import streamlit as st
 import pandas as pd
 import joblib
-from huggingface_hub import hf_hub_download
 
 st.set_page_config(page_title="Predictive Maintenance App", layout="centered")
 
-MODEL_REPO_ID = "nansri/engine-predictive-maintenance-model"
-
 @st.cache_resource
 def load_model():
-    model_path = hf_hub_download(
-        repo_id=MODEL_REPO_ID,
-        filename="best_model.joblib",
-        repo_type="model"
-    )
-    return joblib.load(model_path)
+    return joblib.load("best_model.joblib")
 
 st.title("Predictive Maintenance for Engine Health")
 st.write("Enter the engine sensor values below to predict engine condition.")
@@ -28,19 +21,18 @@ coolant_temp = st.number_input("Coolant Temperature", min_value=0.0, value=80.5)
 
 if st.button("Predict Engine Condition"):
     try:
-        with st.spinner("Loading model and generating prediction..."):
-            model = load_model()
+        model = load_model()
 
-            input_df = pd.DataFrame([{
-                "Engine_rpm": engine_rpm,
-                "Lub_oil_pressure": lub_oil_pressure,
-                "Fuel_pressure": fuel_pressure,
-                "Coolant_pressure": coolant_pressure,
-                "lub_oil_temp": lub_oil_temp,
-                "Coolant_temp": coolant_temp
-            }])
+        input_df = pd.DataFrame([{
+            "Engine_rpm": engine_rpm,
+            "Lub_oil_pressure": lub_oil_pressure,
+            "Fuel_pressure": fuel_pressure,
+            "Coolant_pressure": coolant_pressure,
+            "lub_oil_temp": lub_oil_temp,
+            "Coolant_temp": coolant_temp
+        }])
 
-            prediction = model.predict(input_df)[0]
+        prediction = model.predict(input_df)[0]
 
         if prediction == 1:
             st.error("Prediction: Engine may require maintenance.")
@@ -49,6 +41,6 @@ if st.button("Predict Engine Condition"):
 
         st.write("Input dataframe used for prediction:")
         st.dataframe(input_df)
-    
+
     except Exception as e:
-        st.error(f"Prediction failed: {e}")
+        st.error(f"Prediction failed: {e}")

best_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c93788dcb2a026a9b78690d25140233dc908baae3a2c21232dad97051b09c55
+size 65796

requirements.txt

@@ -1,5 +1,4 @@
 streamlit==1.44.1
 pandas==2.2.3
 joblib==1.4.2
-huggingface_hub==0.30.2
 scikit-learn==1.6.1