stable

Makefile

@@ -3,12 +3,12 @@ install:
 		pip install -r requirements.txt
 
 test:
-	python -m pytest -vv prog_1.py
+	python -m pytest -vv test_app.py
 
 format:
 	black *.py
 
 lint:
-	pylint --disable=R,C prog_1.py
+	pylint --disable=R,C iris_train.py
 
-all: install lint test
+all: install test

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import make_pipeline
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+
+# Load Iris dataset
+iris = load_iris()
+X = iris.data
+y = iris.target
+
+# Split dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Define model
+model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))
+
+# Train model
+model.fit(X_train, y_train)
+
+# Define a function for prediction
+def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
+    features = [[sepal_length, sepal_width, petal_length, petal_width]]
+    prediction = model.predict(features)
+    return iris.target_names[prediction[0]]
+
+# Create Gradio interface
+iris_interface = gr.Interface(
+    fn=predict_iris,
+    inputs=["number", "number", "number", "number"],
+    outputs="text",
+    title="Iris clarrification model",
+    description="Measurements -->  species prediction.",
+    examples=[
+        [5.1, 3.5, 1.4, 0.2],
+        [6.9, 3.1, 5.4, 2.1]
+    ]
+)
+
+# Launch the interface
+iris_interface.launch()

iris_train.py

@@ -0,0 +1,27 @@
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import make_pipeline
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+
+# Load Iris dataset
+iris = load_iris()
+X = iris.data
+y = iris.target
+
+# Split dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Define model
+model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))
+
+# Train model
+model.fit(X_train, y_train)
+
+# Predict
+y_pred = model.predict(X_test)
+
+# Evaluate model
+accuracy = accuracy_score(y_test, y_pred)
+print("Accuracy:", accuracy)

requirements.txt

@@ -1 +1,5 @@
 pytest
+numpy
+scipy
+scikit-learn
+gradio

test_app.py

@@ -0,0 +1,18 @@
+import numpy as np
+from app import predict_iris
+
+def test_predict_iris():
+    # Test with known input values and expected output
+    assert predict_iris(5.1, 3.5, 1.4, 0.2) == "setosa"
+    assert predict_iris(6.9, 3.1, 5.4, 2.1) == "virginica"
+
+    # Test with out-of-range input values
+    assert predict_iris(0, 0, 0, 0) == "setosa"
+    assert predict_iris(10, 10, 10, 10) == "virginica"
+
+    # Test with random input values
+    assert predict_iris(5.8, 2.7, 3.9, 1.2) in ["versicolor", "virginica", "setosa"]
+
+if __name__ == "__main__":
+    test_predict_iris()
+    print("All tests passed!")