changes

app.py

@@ -1,18 +1,14 @@
 import gradio as gr
-import pandas as pd
 import numpy as np
 from sklearn.linear_model import LinearRegression
 import matplotlib.pyplot as plt
 import io
 from PIL import Image
 
-def linear_regression(input_csv, x_column, y_column):
-    # Load dataset from binary
-    df = pd.read_csv(io.BytesIO(input_csv))
-    
-    # Prepare data for regression
-    X = df[[x_column]].values
-    y = df[y_column].values
+def linear_regression(x_values, y_values):
+    # Convert string inputs to numpy arrays
+    X = np.array([float(x) for x in x_values.split(',')]).reshape(-1, 1)
+    y = np.array([float(y) for y in y_values.split(',')])
 
     # Perform linear regression
     model = LinearRegression()
@@ -25,8 +21,8 @@ def linear_regression(input_csv, x_column, y_column):
     plt.figure(figsize=(10, 6))
     plt.scatter(X, y, color='blue')
     plt.plot(X, y_pred, color='red')
-    plt.xlabel(x_column)
-    plt.ylabel(y_column)
+    plt.xlabel("X Values")
+    plt.ylabel("Y Values")
     plt.title('Linear Regression')
 
     # Save plot to a buffer and convert to PIL Image
@@ -44,16 +40,15 @@ def linear_regression(input_csv, x_column, y_column):
 iface = gr.Interface(
     fn=linear_regression,
     inputs=[
-        gr.components.File(type="binary"),
-        gr.components.Textbox(label="X Column Name"),
-        gr.components.Textbox(label="Y Column Name"),
+        gr.components.Textbox(placeholder="Enter X values separated by commas (e.g., 1,2,3)", label="X Values"),
+        gr.components.Textbox(placeholder="Enter Y values separated by commas (e.g., 2,4,6)", label="Y Values")
     ],
     outputs=[
         gr.components.Image(type="pil"),
         gr.components.Textbox(label="Regression Info")
     ],
     title="Automatic Linear Regression Modeling",
-    description="Upload a CSV file and specify the columns for performing linear regression."
+    description="Enter X and Y values as comma-separated lists to perform linear regression."
 )
 
 # Launch the app