Upload 3 files

ProjectFiles.rar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b28d208c32f538cd68920a42eb31497605bf8a5e85da6a4e9a21c62d119b5a9f
+size 890

app.py

@@ -0,0 +1,32 @@
+import gradio as gr
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+
+# 1️⃣ Load dataset and train a simple model
+iris = load_iris()
+X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=42)
+model = RandomForestClassifier()
+model.fit(X_train, y_train)
+
+# 2️⃣ Define prediction function
+def predict(sepal_length, sepal_width, petal_length, petal_width):
+    preds = model.predict([[sepal_length, sepal_width, petal_length, petal_width]])
+    return iris.target_names[preds[0]]
+
+# 3️⃣ Create Gradio interface
+inputs = [
+    gr.Number(label="Sepal length (cm)"),
+    gr.Number(label="Sepal width (cm)"),
+    gr.Number(label="Petal length (cm)"),
+    gr.Number(label="Petal width (cm)")
+]
+output = gr.Textbox(label="Predicted Iris Species")
+
+demo = gr.Interface(fn=predict, inputs=inputs, outputs=output,
+                    title="🌸 Iris Flower Classifier",
+                    description="Predicts the Iris species from flower dimensions.")
+
+# 4️⃣ Launch app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,2 @@
+gradio
+scikit-learn