Fresh deployment

app.py

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+# deployment/app_gradio.py
+import gradio as gr
+import joblib
+import numpy as np
+
+# Load model and scaler
+model = joblib.load("models/best_model.pkl")
+scaler = joblib.load("models/scaler.pkl")
+
+def predict(Variance_Wavelet, Skewness_Wavelet, Curtosis_Wavelet, Image_Entropy):
+    features = np.array([[Variance_Wavelet, Skewness_Wavelet, Curtosis_Wavelet, Image_Entropy]])
+    scaled = scaler.transform(features)
+    prediction = model.predict(scaled)
+    return "Authentic" if prediction[0] == 1 else "Forged"
+
+# Define Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Variance of Wavelet"),
+        gr.Number(label="Skewness of Wavelet"),
+        gr.Number(label="Curtosis of Wavelet"),
+        gr.Number(label="Entropy of Image")
+    ],
+    outputs="text",
+    title="Banknote Authentication",
+    description="Enter the banknote features to check if it's authentic or forged."
+)
+
+if __name__ == "__main__":
+    interface.launch(share=True)
+
+# To run the Gradio app, execute: python deployment/app_gradio.py  
+# Then open the provided link in your browser to interact with the model.
+# Make sure you have the model and scaler files in the "models" directory.

models/best_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:01fb85f3eba6a2d0d599622c221048486d4f7620be328f886089d7a20ddb540b
+size 553530

models/scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:da59c2581cc513f39b2c4df628a2624d15399f375b740f5f43a12e3c6f46cc70
+size 1031

models/voting_classifier.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:35586b24f9247c798e08106594b5354daae41308a4401eba1425a26ff8b181ba
+size 512111

requirements.txt

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+# Core ML & Data
+numpy==1.26.4
+pandas==2.3.0
+scikit-learn==1.7.0
+xgboost==2.0.3
+joblib==1.4.2
+
+# Visualization (if needed)
+matplotlib==3.8.4
+seaborn==0.13.2
+
+# Deployment
+gradio==5.35.0
+streamlit==1.35.0
+
+# Logging and utility
+loguru==0.7.3
+# Testingpython-dotenv==1.1.1
+pandas==2.3.0
+scikit-learn==1.7.0
+pytest==8.4.1