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2ec5040 | 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 | 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()
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