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d7e53e8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | import gradio as gr
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import (
accuracy_score,
mean_absolute_error,
mean_squared_error,
r2_score,
)
import numpy as np
# ======================
# Model imports
# ======================
from sklearn.linear_model import (
LinearRegression,
LogisticRegression,
Perceptron,
)
from sklearn.neighbors import (
KNeighborsClassifier,
KNeighborsRegressor,
)
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import (
DecisionTreeClassifier,
DecisionTreeRegressor,
)
from sklearn.svm import SVC, SVR
from sklearn.neural_network import (
MLPClassifier,
MLPRegressor,
)
# ======================
# Model registry
# ======================
REGRESSION_MODELS = {
"Linear Regression": LinearRegression,
"KNN Regressor": KNeighborsRegressor,
"Decision Tree Regressor": DecisionTreeRegressor,
"SVR": SVR,
"MLP Regressor": MLPRegressor,
}
CLASSIFICATION_MODELS = {
"Logistic Regression": LogisticRegression,
"KNN Classifier": KNeighborsClassifier,
"Naive Bayes": GaussianNB,
"Perceptron": Perceptron,
"Decision Tree Classifier": DecisionTreeClassifier,
"SVM Classifier": SVC,
"MLP Classifier": MLPClassifier,
}
# ======================
# UI Logic
# ======================
def update_models(task_type):
if task_type == "Regression":
return gr.update(choices=list(REGRESSION_MODELS.keys()), value=None)
return gr.update(choices=list(CLASSIFICATION_MODELS.keys()), value=None)
def train_model(file, task_type, model_name):
df = pd.read_csv(file.name)
# Assumption: last column is target
X = df.iloc[:, :-1]
y = df.iloc[:, -1]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
if task_type == "Regression":
model = REGRESSION_MODELS[model_name]()
model.fit(X_train, y_train)
preds = model.predict(X_test)
mae = mean_absolute_error(y_test, preds)
mse = mean_squared_error(y_test, preds)
rmse = np.sqrt(mse)
r2 = r2_score(y_test, preds)
return (
f"Model: {model_name}\n"
f"Task: Regression\n"
f"MAE: {mae:.4f}\n"
f"MSE: {mse:.4f}\n"
f"RMSE: {rmse:.4f}\n"
f"R² Score: {r2:.4f}"
)
else:
model = CLASSIFICATION_MODELS[model_name]()
model.fit(X_train, y_train)
preds = model.predict(X_test)
acc = accuracy_score(y_test, preds)
return (
f"Model: {model_name}\n"
f"Task: Classification\n"
f"Accuracy: {acc:.4f}"
)
# ======================
# Gradio App
# ======================
with gr.Blocks() as demo:
gr.Markdown("## Supervised Learning Model Trainer")
gr.Markdown(
"Upload a CSV file. The **last column is treated as target**."
)
file_input = gr.File(label="Upload CSV", file_types=[".csv"])
task_dropdown = gr.Dropdown(
["Regression", "Classification"],
label="Task Type",
)
model_dropdown = gr.Dropdown(label="Model")
output = gr.Textbox(label="Result", lines=5)
train_btn = gr.Button("Generate")
task_dropdown.change(
update_models,
inputs=task_dropdown,
outputs=model_dropdown,
)
train_btn.click(
train_model,
inputs=[file_input, task_dropdown, model_dropdown],
outputs=output,
)
demo.launch() |