Upload folder using huggingface_hub

Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9-slim
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Define the command to run the Streamlit app on port 8501 and make it accessible externally
+# CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

README.md

@@ -1,11 +1,35 @@
 ---
-title: Engine Condition Prediction Space
-emoji: 📊
-colorFrom: gray
-colorTo: yellow
+title: Engine Condition Prediction
 sdk: docker
-pinned: false
-short_description: Engine-Condition-Prediction-Space
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Engine Condition Prediction 
+
+This Streamlit application predicts the **engine condition (Normal or Faulty)** using an **XGBoost machine learning model**.
+
+## Model Details
+- **Algorithm**: XGBoost Classifier
+- **Model Source**: Hugging Face Model Hub
+- **Input Features**:
+  - Engine rpm
+  - Lub oil pressure
+  - Fuel pressure
+  - Coolant pressure
+  - lub oil temp
+  - Coolant temp
+
+## How It Works
+1. User enters real-time engine sensor values.
+2. The app loads a pre-trained XGBoost model from Hugging Face.
+3. The model predicts the engine condition.
+4. Inputs and predictions are stored in a CSV file for logging.
+
+## Deployment
+- **Framework**: Streamlit
+- **Containerized with**: Docker
+- **Hosted on**: Hugging Face Spaces
+
+## Dependencies
+All dependencies are defined in `requirements.txt` and installed during Docker build.
+
+---

app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+st.set_page_config(page_title="Engine Condition Prediction")
+
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(
+        repo_id="ShanRaja/engine-fault-xgboost",
+        filename="best_model.joblib"
+    )
+    return joblib.load(model_path)
+
+model = load_model()
+
+st.title("Engine Condition Prediction")
+
+engine_rpm = st.number_input("Engine rpm", min_value=0, step=100)
+lub_oil_pressure = st.number_input("Lub oil pressure", format="%.3f")
+fuel_pressure = st.number_input("Fuel pressure", format="%.3f")
+coolant_pressure = st.number_input("Coolant pressure", format="%.3f")
+lub_oil_temp = st.number_input("lub oil temp", format="%.2f")
+coolant_temp = st.number_input("Coolant temp", format="%.2f")
+
+if st.button("Predict"):
+    input_df = pd.DataFrame(
+        [[
+            int(engine_rpm),
+            lub_oil_pressure,
+            fuel_pressure,
+            coolant_pressure,
+            lub_oil_temp,
+            coolant_temp
+        ]],
+        columns=[
+            "Engine rpm",
+            "Lub oil pressure",
+            "Fuel pressure",
+            "Coolant pressure",
+            "lub oil temp",
+            "Coolant temp"
+        ]
+    )
+
+    prediction = model.predict(input_df)[0]
+
+    label = "NORMAL" if prediction == 1 else "FAULTY"
+
+    st.success(f"Engine Condition: {label}")
+
+    # Save input + prediction
+    input_df["Engine Condition"] = prediction
+    input_df.to_csv(
+        "inputs.csv",
+        mode="a",
+        header=not st.session_state.get("logged", False),
+        index=False
+    )
+    st.session_state["logged"] = True

push_to_hf.py

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+from huggingface_hub import HfApi, upload_folder
+
+# HUGGING FACE CONFIGURATION
+HF_USERNAME = "ShanRaja"
+SPACE_NAME = "Engine-Condition-Prediction-Space"   # Name of the HF Space
+
+REPO_ID = f"{HF_USERNAME}/{SPACE_NAME}"
+
+# CREATE OR UPDATE THE HF SPACE
+api = HfApi()
+
+print("Creating or updating Hugging Face Space...")
+
+api.create_repo(
+    repo_id=REPO_ID,
+    repo_type="space",
+    space_sdk="docker",
+    exist_ok=True
+)
+
+# UPLOAD DEPLOYMENT FILES
+print("Uploading deployment files...")
+
+upload_folder(
+    folder_path="deployment_files",
+    repo_id=REPO_ID,
+    repo_type="space",
+    #ignore_patterns=["__pycache__", "*.ipynb"]
+)
+
+print("Deployment completed successfully!")
+print(f" The Application is live at: https://huggingface.co/spaces/{REPO_ID}")

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.5.3
+streamlit==1.53.0
+huggingface_hub==0.27.1