Upload folder using huggingface_hub

app.py

@@ -1,60 +1,78 @@
+# %%writefile app.py
 import streamlit as st
 import pandas as pd
 import joblib
 from huggingface_hub import hf_hub_download
+from sklearn.ensemble import AdaBoostClassifier  # ensure scikit-learn flavor
+from sklearn.metrics import classification_report
 
-# ----------------------------
-# Load model from Hugging Face
-# ----------------------------
-HF_MODEL_REPO = "vihu21/predictive_maintenance"
-MODEL_FILE = "best_ada_model.joblib"
-
-model_path = hf_hub_download(
-    repo_id="vihu21/predictive_maintenance",
-    filename="best_ada_model.joblib",
-    repo_type="model"
-)
-
-model = joblib.load(model_path)
-
-# ----------------------------
-# Streamlit UI
-# ----------------------------
-st.title("🔧 Engine Condition Prediction")
-st.write("Fill in the engine parameters to predict engine condition.")
-
-# ----------------------------
-# User Inputs
-# ----------------------------
-engine_rpm = st.number_input("Engine RPM", min_value=50, max_value=6000, value=3000)
-lub_oil_pressure = st.number_input("Lub Oil Pressure", min_value=0.0, value=7.25)
-fuel_pressure = st.number_input("Fuel Pressure", min_value=0.0, value=21.4)
-coolant_pressure = st.number_input("Coolant Pressure", min_value=0.0, value=7.5)
-lub_oil_temp = st.number_input("Lub Oil Temperature", min_value=50.0, value=90.0)
-coolant_temp = st.number_input("Coolant Temperature", min_value=60.0, value=195.0)
-
-# ----------------------------
-# Prepare input data
-# ⚠️ Column names must EXACTLY match training
-# ----------------------------
+# -------------------------------
+# Streamlit App Title & Description
+# -------------------------------
+st.set_page_config(page_title="Engine Condition Predictor", layout="centered")
+st.title("Engine Condition Prediction")
+st.write("""
+Predict if an engine's condition is Good or Bad based on sensor readings.
+""")
+
+# -------------------------------
+# Load the model from Hugging Face
+# -------------------------------
+
+@st.cache_resource(show_spinner=True)
+def load_model():
+    """
+    Load the pre-trained AdaBoost model from Hugging Face.
+    Uses caching to prevent re-downloading every restart.
+    """
+    HF_DATASET = "vihu21/predictive_maintenance"
+
+    MODEL_FILE = "engine_predict/best_ada_model.joblib"   # ⚠️ make sure this matches HF exactly
+
+    model_path = hf_hub_download(
+      repo_id=HF_DATASET,
+      filename=MODEL_FILE,
+      repo_type="model"
+    )
+    return joblib.load(model_path)
+
+
+
+ada_model = load_model()
+st.success("✅ Model loaded successfully!")
+
+# -------------------------------
+# User Input
+# -------------------------------
+st.subheader("Engine Sensor Inputs")
+Engine_Details = st.number_input("Engine Details Size (MB)", min_value=1.0, max_value=4000.0, value=50.0, step=0.1)
+EngineRpm = st.number_input("Engine rpm", min_value=50, max_value=10000, value=3000)
+LubOilPressure = st.number_input("Lubrication Oil Pressure", min_value=0.0, value=7.25)
+FuelPressure = st.number_input("Fuel Pressure", min_value=0.0, value=21.4)
+CoolantPressure = st.number_input("Coolant Pressure", min_value=0.0, value=7.5)
+LubOilTemp = st.number_input("Lubrication Oil Temperature", min_value=0.0, value=90.0)
+CoolantTemp = st.number_input("Coolant Temperature", min_value=0.0, value=195.0)
+
+# -------------------------------
+# Prepare input dataframe
+# -------------------------------
 input_data = 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
+    'Engine rpm': EngineRpm,
+    'Lub oil pressure': LubOilPressure,
+    'Fuel pressure': FuelPressure,
+    'Coolant pressure': CoolantPressure,
+    'lub oil temp': LubOilTemp,
+    'Coolant temp': CoolantTemp
 }])
 
-# ----------------------------
-# Prediction
-# ----------------------------
+# -------------------------------
+# Make Prediction
+# -------------------------------
 if st.button("Predict Engine Condition"):
-    prediction = model.predict(input_data)[0]
-
-    st.subheader("Prediction Result")
+    try:
+        prediction = ada_model.predict(input_data)[0]
+        st.subheader("Prediction Result")
+        st.success(f"Estimated Engine Condition: **{prediction}**")
+    except Exception as e:
+        st.error(f"❌ Error during prediction: {str(e)}")
 
-    if prediction == 1 or prediction == "Good":
-        st.success("✅ Engine Condition: GOOD")
-    else:
-        st.error("⚠️ Engine Condition: BAD")