Upload 3 files

Initial deployment: Iris Model with Gradio interface.

app.py

@@ -0,0 +1,31 @@
+# Bismillahir Rahmaanir Raheem
+# Almadadh Ya Gause Radi Allahu Ta'alah Anh - Ameen
+
+
+import pickle
+import gradio as gr
+
+# Load the trained model from .pkl file
+with open('iris_decision_tree_model.pkl', 'rb') as file:
+    clf = pickle.load(file)
+
+# Import iris dataset for target names
+from sklearn import datasets
+iris = datasets.load_iris()
+
+# Define the prediction function
+def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
+    prediction = clf.predict([[sepal_length, sepal_width, petal_length, petal_width]])
+    return iris.target_names[int(prediction[0])]
+
+# Create and launch the Gradio interface
+interface = gr.Interface(
+    fn=predict_iris, 
+    inputs=["number", "number", "number", "number"], 
+    outputs="text",
+    live=True,
+    title="Iris Flower Model",
+    description="An introductory example of machine learning in Python. An iris flower model trained on the iris flower dataset using the decision tree algorithm. The accuracy of the model is: 97.37%. Input the dimensions of the iris flower's sepal and petal to predict its species."
+)
+
+interface.launch()

iris_decision_tree_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:278efa90d037ad357b233c676e73cbe6da6427c96fc5bcf3afb629c5e3beed19
+size 2273

requirements.txt

@@ -0,0 +1,4 @@
+numpy
+pandas
+sklearn
+gradio