Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, classification_report
+from sklearn.preprocessing import StandardScaler
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.svm import SVC
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.tree import DecisionTreeClassifier
+
+import mlflow
+import mlflow.sklearn
+
+# ----------------------------
+# Load Iris Data
+# ----------------------------
+iris = load_iris()
+df = pd.DataFrame(iris.data, columns=iris.feature_names)
+df["target"] = iris.target
+
+# Scale data
+scaler = StandardScaler()
+x_scaled = scaler.fit_transform(iris.data)
+y = iris.target
+
+# Split
+x_train, x_test, y_train, y_test = train_test_split(
+    x_scaled, y, test_size=0.2, random_state=42
+)
+
+# ----------------------------
+# MLflow Setup
+# ----------------------------
+mlflow.set_experiment("iris-Experiment")
+
+models = {
+    "Logistic Regression": LogisticRegression(),
+    "KNN (k=3)": KNeighborsClassifier(n_neighbors=3),
+    "Decision Tree": DecisionTreeClassifier(),
+    "Linear SVM": SVC(kernel="linear"),
+}
+
+trained_models = {}
+
+def train_and_log(model, name):
+    with mlflow.start_run(run_name=name):
+        model.fit(x_train, y_train)
+        y_pred = model.predict(x_test)
+
+        acc = accuracy_score(y_test, y_pred)
+
+        mlflow.log_param("model_name", name)
+        mlflow.log_metric("accuracy", acc)
+        mlflow.sklearn.log_model(model, name)
+
+        trained_models[name] = model
+        print(f"Trained {name} - Accuracy: {acc}")
+
+for name, model in models.items():
+    train_and_log(model, name)
+
+# ----------------------------
+# Visualization Function
+# ----------------------------
+def plot_pairplot():
+    plt.figure()
+    sns.pairplot(df, hue="target")
+    plt.tight_layout()
+    plt.savefig("pairplot.png")
+    return "pairplot.png"
+
+# ----------------------------
+# Prediction Function
+# ----------------------------
+def predict(model_name, sepal_length, sepal_width, petal_length, petal_width):
+    model = trained_models[model_name]
+
+    input_data = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
+    input_scaled = scaler.transform(input_data)
+
+    pred = model.predict(input_scaled)[0]
+    class_name = iris.target_names[pred]
+
+    return f"Predicted Class: {class_name}"
+
+# ----------------------------
+# Build Gradio UI
+# ----------------------------
+with gr.Blocks() as demo:
+    gr.Markdown("## 🌸 Iris Flower Classification (MLflow + Gradio)")
+
+    with gr.Row():
+        model_choice = gr.Dropdown(
+            list(trained_models.keys()), label="Select ML Model"
+        )
+
+    with gr.Row():
+        sepal_length = gr.Number(label="Sepal Length")
+        sepal_width = gr.Number(label="Sepal Width")
+        petal_length = gr.Number(label="Petal Length")
+        petal_width = gr.Number(label="Petal Width")
+
+    predict_btn = gr.Button("Predict")
+    output_text = gr.Textbox(label="Prediction Result")
+
+    predict_btn.click(
+        predict,
+        inputs=[model_choice, sepal_length, sepal_width, petal_length, petal_width],
+        outputs=output_text,
+    )
+
+    gr.Markdown("### 📊 Dataset Visualization")
+    viz_btn = gr.Button("Show Pairplot")
+    viz_output = gr.Image()
+
+    viz_btn.click(plot_pairplot, outputs=viz_output)
+
+# Launch
+demo.launch()