Update app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+import numpy as np
+import sklearn.datasets as d
+from sklearn.linear_model import *
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import *
+from sklearn.utils import all_estimators
+
+
+import inspect
+import pandas as pd
+import sklearn.metrics as m
+
+def predict(dataset, model, split, metrics):
+  pass
+  models = [cls for cls in all_estimators() if cls[0] == model]
+  if len(models) == 0:
+    return "Model not found"
+  model = models[0][1]()
+  data = getattr(d, dataset)()
+  X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=float(split))
+  scaler = StandardScaler()
+  X_train = scaler.fit_transform(X_train)
+  X_test = scaler.transform(X_test)
+  model.fit(X_train, y_train)
+  pred = model.predict(X_test)
+  df = pd.DataFrame(data.data, columns=data.feature_names)
+  df["target"] = data.target
+  scores = []
+  for metric in metrics:
+    try:
+      if hasattr(m, metric):
+        scores.append((metric, getattr(m, metric)(y_test, pred)))
+    except:
+      pass
+  scoress = pd.DataFrame(scores, columns=["metric", "score"])
+  return gr.Dataframe(scoress, headers=scoress.columns.tolist(), datatype=["numeric"] * len(df.columns))
+
+demo = gr.Interface(fn=predict, inputs=[
+    gr.Dropdown([ name for name, obj in inspect.getmembers(d)
+    if inspect.isfunction(obj) and not name.startswith("_")], value="load_breast_cancer", label="Dataset"), 
+    gr.Dropdown([name for name, cls in all_estimators()], value="RandomForestClassifier", label="Model"), 
+    gr.Textbox(value="0.2", label="TrainTest Split"),
+    gr.CheckboxGroup([n for n in dir(m) if callable(getattr(m, n)) and not n.startswith("_")], label="metrics", value="accuracy_score")
+], outputs="dataframe")
+
+demo.launch(share=True, debug=True)