"hello"

iris_flower_classifier.py

@@ -0,0 +1,54 @@
+# -*- coding: utf-8 -*-
+"""Iris_Flower_Classifier.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1ddsKnOLQk_nPeF9zu0Qr9yTsvmg-0D8S
+"""
+
+import gradio as gr
+import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.pipeline import make_pipeline
+
+# Load the Iris dataset
+iris = load_iris()
+X = iris.data
+y = iris.target
+feature_names = iris.feature_names
+target_names = iris.target_names
+
+# Split the data 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)
+
+# Create and train a RandomForest model
+model = make_pipeline(StandardScaler(), RandomForestClassifier())
+model.fit(X_train, y_train)
+
+# Define the prediction function
+def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
+    feature_values = np.array([sepal_length, sepal_width, petal_length, petal_width]).reshape(1, -1)
+    prediction = model.predict(feature_values)
+    return target_names[prediction[0]]
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict_iris,
+    inputs=[
+        gr.Slider(minimum=float(X[:, 0].min()), maximum=float(X[:, 0].max()), value=float(np.mean(X[:, 0])), label="Sepal Length (cm)"), # Changed 'default' to 'value'
+        gr.Slider(minimum=float(X[:, 1].min()), maximum=float(X[:, 1].max()), value=float(np.mean(X[:, 1])), label="Sepal Width (cm)"), # Changed 'default' to 'value'
+        gr.Slider(minimum=float(X[:, 2].min()), maximum=float(X[:, 2].max()), value=float(np.mean(X[:, 2])), label="Petal Length (cm)"), # Changed 'default' to 'value'
+        gr.Slider(minimum=float(X[:, 3].min()), maximum=float(X[:, 3].max()), value=float(np.mean(X[:, 3])), label="Petal Width (cm)") # Changed 'default' to 'value'
+    ],
+    outputs="text",
+    title="Iris Flower Classifier",
+    description="Select the features of the iris flower to predict its species."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch()