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import pandas as pd
import numpy as np
import gradio as gr
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
# Loading the dataset
df = pd.read_csv('assignment-2-k2461469.csv')
# Splitting the data into features and target variable
X = df[["dirty", "wait", "lastyear", "usa"]]
y = df["good"]
# Splitting the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Creating and fitting the logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)
# Function to make predictions and display them on a graph
def predict_and_plot(dirty, wait, lastyear, usa):
# Making prediction for a single input
input_data = np.array([[dirty, wait, lastyear, usa]])
predicted_value = model.predict(input_data)[0]
# Predicting on test set for comparison
y_pred = model.predict(X_test)
# Plotting actual vs predicted values
plt.figure(figsize=(8, 6))
plt.scatter(range(len(y_test)), y_test, color='blue', label='Actual Values', alpha=0.6)
plt.scatter(range(len(y_pred)), y_pred, color='red', label='Predicted Values', alpha=0.6)
plt.title('Actual vs Predicted Values')
plt.xlabel('Sample Index')
plt.ylabel('Value')
plt.legend()
plt.grid(True)
# Save plot to a file and display
plt.savefig('output_plot.png')
plt.close()
return predicted_value, 'output_plot.png'
# Creating Gradio UI
with gr.Blocks() as demo:
gr.Markdown("# Logistic Regression Prediction")
with gr.Row():
dirty_slider = gr.Slider(minimum=0, maximum=1, step=0.01, label="Dirty")
wait_slider = gr.Slider(minimum=0, maximum=1, step=0.01, label="Wait")
lastyear_slider = gr.Slider(minimum=0, maximum=1, step=0.01, label="Last Year")
usa_slider = gr.Slider(minimum=0, maximum=1, step=0.01, label="USA")
predict_button = gr.Button("Predict")
predicted_value_output = gr.Textbox(label="Predicted Value")
plot_output = gr.Image(label="Actual vs Predicted Graph")
predict_button.click(
fn=predict_and_plot,
inputs=[dirty_slider, wait_slider, lastyear_slider, usa_slider],
outputs=[predicted_value_output, plot_output]
)
demo.launch()
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