Update app.py

app.py

@@ -0,0 +1,53 @@
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+import numpy as np
+
+# Title
+st.title("Graph Plotter with Linear Regression")
+
+# File upload
+uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
+
+if uploaded_file:
+    # Load the CSV file
+    data = pd.read_csv(uploaded_file)
+    st.write("Preview of the data:")
+    st.write(data.head())
+
+    # Select columns for X and Y
+    numeric_columns = data.select_dtypes(include=['float64', 'int64']).columns
+    if len(numeric_columns) < 2:
+        st.error("The dataset must have at least two numerical columns.")
+    else:
+        x_col = st.selectbox("Select the X column", numeric_columns)
+        y_col = st.selectbox("Select the Y column", numeric_columns)
+
+        if x_col and y_col:
+            # Prepare data for regression
+            X = data[[x_col]].values
+            Y = data[y_col].values
+
+            # Linear Regression
+            model = LinearRegression()
+            model.fit(X, Y)
+            slope = model.coef_[0]
+            intercept = model.intercept_
+
+            # Display slope and intercept
+            st.write(f"**Slope:** {slope}")
+            st.write(f"**Intercept:** {intercept}")
+
+            # Plot the graph
+            fig, ax = plt.subplots()
+            ax.scatter(X, Y, label="Data points", color="blue")
+            ax.plot(X, model.predict(X), color="red", label="Regression line")
+            ax.set_xlabel(x_col)
+            ax.set_ylabel(y_col)
+            ax.legend()
+            ax.grid(True)
+
+            # Show plot
+            st.pyplot(fig)
+