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| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import numpy as np | |
| from sklearn.datasets import load_iris | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.metrics import accuracy_score, confusion_matrix | |
| # --- Step 1: Load dataset --- | |
| data = load_iris() | |
| X, y = data.data, data.target | |
| feature_names = data.feature_names | |
| target_names = data.target_names | |
| # --- Step 2: Split dataset --- | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| # --- Step 3: Train model --- | |
| model = LogisticRegression(max_iter=200) | |
| model.fit(X_train, y_train) | |
| # --- Step 4: Evaluate model --- | |
| y_pred = model.predict(X_test) | |
| accuracy = accuracy_score(y_test, y_pred) | |
| conf_mat = confusion_matrix(y_test, y_pred) | |
| # --- Step 5: Graph 1 - Confusion Matrix --- | |
| def plot_confusion_matrix(): | |
| fig, ax = plt.subplots(figsize=(4, 3)) | |
| sns.heatmap(conf_mat, annot=True, fmt="d", cmap="Blues", | |
| xticklabels=target_names, yticklabels=target_names) | |
| plt.xlabel("Predicted") | |
| plt.ylabel("Actual") | |
| plt.title("Confusion Matrix") | |
| fig.canvas.draw() | |
| img = np.array(fig.canvas.renderer.buffer_rgba()) | |
| plt.close(fig) | |
| return img | |
| # --- Step 6: Graph 2 - Feature Importance --- | |
| def plot_feature_importance(): | |
| importance = np.abs(model.coef_).mean(axis=0) | |
| fig, ax = plt.subplots(figsize=(5, 3)) | |
| sns.barplot(x=importance, y=feature_names, palette="viridis") | |
| plt.title("Feature Importance") | |
| fig.canvas.draw() | |
| img = np.array(fig.canvas.renderer.buffer_rgba()) | |
| plt.close(fig) | |
| return img | |
| # --- Step 7: Prediction Function --- | |
| def predict_flower(sepal_length, sepal_width, petal_length, petal_width): | |
| features = [[sepal_length, sepal_width, petal_length, petal_width]] | |
| prediction = model.predict(features)[0] | |
| class_name = target_names[prediction] | |
| return f"🌸 Predicted Flower: {class_name}" | |
| # --- Step 8: Gradio Interface --- | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 🌼 Iris Flower Classifier with Graphs") | |
| gr.Markdown(f"**Model Accuracy:** {accuracy:.2f}") | |
| with gr.Row(): | |
| sepal_length = gr.Number(label="Sepal Length (cm)") | |
| sepal_width = gr.Number(label="Sepal Width (cm)") | |
| petal_length = gr.Number(label="Petal Length (cm)") | |
| petal_width = gr.Number(label="Petal Width (cm)") | |
| output_text = gr.Textbox(label="Prediction Result") | |
| predict_btn = gr.Button("🔍 Predict") | |
| predict_btn.click( | |
| predict_flower, | |
| inputs=[sepal_length, sepal_width, petal_length, petal_width], | |
| outputs=output_text | |
| ) | |
| gr.Markdown("### 📊 Model Performance Graphs") | |
| gr.Image(value=plot_confusion_matrix(), label="Confusion Matrix") | |
| gr.Image(value=plot_feature_importance(), label="Feature Importance") | |
| # --- Step 9: Launch the app --- | |
| demo.launch() | |