first commit

app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+from sklearn.datasets import load_iris
+
+# Load model and scaler
+model = joblib.load('models/iris_model.joblib')
+scaler = joblib.load('models/scaler.joblib')
+iris = load_iris()
+
+# Feature names
+features = iris.feature_names
+
+# Prediction function
+def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
+    input_data = pd.DataFrame([[sepal_length, sepal_width, petal_length, petal_width]], columns=features)
+    input_data_scaled = scaler.transform(input_data)
+    prediction = model.predict(input_data_scaled)
+    probabilities = model.predict_proba(input_data_scaled)[0]
+    species = iris.target_names[prediction[0]]
+    return (
+        f"Predicted Species: {species}",
+        {iris.target_names[i]: f"{prob:.2f}" for i, prob in enumerate(probabilities)}
+    )
+
+# Gradio interface
+inputs = [
+    gr.Slider(0, 10, 5.0, label="Sepal Length (cm)"),
+    gr.Slider(0, 10, 3.5, label="Sepal Width (cm)"),
+    gr.Slider(0, 10, 1.4, label="Petal Length (cm)"),
+    gr.Slider(0, 10, 0.2, label="Petal Width (cm)")
+]
+outputs = [gr.Textbox(label="Prediction"), gr.Label(label="Confidence Scores")]
+gr.Interface(fn=predict_iris, inputs=inputs, outputs=outputs, title="Iris Species Prediction").launch()

models/iris_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c201be3cf668244dbf62a7b12d3d5cf09c00e08baf10c06993644170b56fb1fa
+size 991

models/scaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aa294d8111a148c835f16e383756d3a9aec4f10d8278febbe8ede2cd6abd791c
+size 1047

requirements.txt

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+gradio
+pandas
+scikit-learn
+joblib