Initial commit

app.py

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+import joblib
+import gradio as gr
+import numpy as np
+
+# Load the model
+model = joblib.load("model.joblib")
+
+# Define prediction function
+def predict(input1, input2, input3):  # adjust based on your features
+    X = np.array([[input1, input2, input3]])
+    prediction = model.predict(X)
+    return prediction[0]
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Feature 1"),
+        gr.Number(label="Feature 2"),
+        gr.Number(label="Feature 3")
+    ],
+    outputs="text",
+    title="My Sklearn Model"
+)
+
+# Run Gradio app
+if __name__ == "__main__":
+    demo.launch()

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8e9c3d63e145ca426814b275b7d3f5f60169e0992b3044dcef2b7a3f892333fd
+size 534162

requirements.txt

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+scikit-learn
+gradio
+joblib
+numpy