UI code added

app.py

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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()

requirements.txt

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+pandas
+numpy
+gradio
+scikit-learn
+matplotlib