Deploy predictive maintenance Streamlit app

Dockerfile

@@ -6,7 +6,8 @@ ENV PYTHONUNBUFFERED=1
 WORKDIR /app
 
 COPY requirements.txt /app/requirements.txt
-RUN pip install --no-cache-dir --upgrade pip &&     pip install --no-cache-dir -r /app/requirements.txt
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r /app/requirements.txt
 
 COPY app.py /app/app.py
 

app.py

@@ -16,7 +16,6 @@ LABEL_MAP = {
 
 @st.cache_resource
 def load_model():
-    # Download and load the trained model from Hugging Face Model Hub.
     token = os.getenv("HF_TOKEN")
     model_path = hf_hub_download(
         repo_id=HF_MODEL_REPO_ID,
@@ -55,9 +54,7 @@ with st.form("prediction_form"):
     submitted = st.form_submit_button("Predict Engine Condition")
 
 if submitted:
-    # Rubric requirement: get inputs and save them into a DataFrame.
     input_df = pd.DataFrame([sensor_values], columns=FEATURE_COLUMNS)
-
     prediction = int(MODEL.predict(input_df)[0])
 
     if hasattr(MODEL, "predict_proba"):