Upload folder using huggingface_hub

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+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
+import matplotlib.pyplot as plt
+from huggingface_hub import hf_hub_download
+
+# -----------------------------------------
+# Load Model from Hugging Face Model Hub
+# -----------------------------------------
+
+REPO_ID = "SunnyShaurya/engine-condition-classifier"
+
+model_path = hf_hub_download(
+    repo_id=REPO_ID,
+    filename="engine_condition_rf_production.joblib"
+)
+
+threshold_path = hf_hub_download(
+    repo_id=REPO_ID,
+    filename="decision_threshold.joblib"
+)
+
+model = joblib.load(model_path)
+saved_threshold = joblib.load(threshold_path)
+
+feature_names = model.feature_names_in_
+
+# -----------------------------------------
+# Streamlit UI
+# -----------------------------------------
+
+st.title("Engine Condition Classification System")
+
+st.markdown("### Adjust Decision Threshold")
+user_threshold = st.slider(
+    "Decision Threshold",
+    min_value=0.1,
+    max_value=0.9,
+    value=float(saved_threshold),
+    step=0.01
+)
+
+# -----------------------------------------
+# Single Prediction Section
+# -----------------------------------------
+
+st.markdown("## Manual Engine Input")
+
+input_data = []
+
+for feature in feature_names:
+    value = st.number_input(f"{feature}", value=0.0)
+    input_data.append(value)
+
+if st.button("Predict Engine Condition"):
+
+    input_df = pd.DataFrame([input_data], columns=feature_names)
+    probability = model.predict_proba(input_df)[0][1]
+    prediction = 1 if probability >= user_threshold else 0
+
+    st.write("### Probability of Failure:", round(probability, 4))
+    st.write(f"Model Confidence: {round(probability*100,2)}%")
+
+    # Explanation Logic
+    if probability > 0.75:
+        st.info("High risk detected. Immediate inspection recommended.")
+    elif probability > 0.55:
+        st.warning("Moderate risk. Preventive check advised.")
+    else:
+        st.success("Low risk. Engine likely operating normally.")
+
+    if prediction == 1:
+        st.error("⚠ Engine Likely Faulty")
+    else:
+        st.success("✅ Engine Operating Normally")
+
+    # -----------------------------------------
+    # Confidence Visualization
+    # -----------------------------------------
+
+    fig, ax = plt.subplots()
+    ax.bar(["Normal Probability", "Failure Probability"],
+           [1 - probability, probability])
+    ax.set_ylim(0, 1)
+    ax.set_ylabel("Probability")
+    st.pyplot(fig)
+
+# -----------------------------------------
+# Batch Prediction (CSV Upload)
+# -----------------------------------------
+
+st.markdown("## Batch Prediction (CSV Upload)")
+
+uploaded_file = st.file_uploader("Upload CSV File", type=["csv"])
+
+if uploaded_file is not None:
+
+    df = pd.read_csv(uploaded_file)
+
+    # Ensure columns match training features
+    df = df[feature_names]
+
+    probabilities = model.predict_proba(df)[:, 1]
+
+    df["Probability_of_Failure"] = probabilities
+    df["Prediction"] = (probabilities >= user_threshold).astype(int)
+
+    st.write("### Prediction Results")
+    st.write(df.head())
+
+    csv = df.to_csv(index=False).encode("utf-8")
+
+    st.download_button(
+        "Download Predictions",
+        csv,
+        "engine_predictions.csv",
+        "text/csv"
+    )

requirements.txt

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+streamlit
+pandas
+numpy
+scikit-learn
+matplotlib
+joblib