Update app.py

app.py

@@ -3,53 +3,36 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 # Streamlit app layout
-st.title("Analyzing two sets of data")
+st.title("Random Sample Generator and Statistics Calculator")
 
 with st.form("my_form"):
-    N = st.number_input("How many data points do you want for each set?", step=1)
-
-    # Button to generate the data
-    if st.form_submit_button("Generate Data"):
-        # Generate two sets of random data points
-        set1 = np.random.normal(loc=10, scale=2, size=N)
-        set2 = np.random.normal(loc=12, scale=3, size=N)
-
-    # Display the sets in the app
-    # st.write('Set 1:', set1)
-    # st.write('Set 2:', set2)
-
-if "set1" in globals():
-    mean_set1 = np.mean(set1)
-    mean_set2 = np.mean(set2)
-
-    st.subheader("Means")
-    st.write(f"Mean of Set 1: {mean_set1:.2f}")
-    st.write(f"Mean of Set 2: {mean_set2:.2f}")
-
-    var_set1 = np.var(set1)
-    var_set2 = np.var(set2)
-
-    st.subheader("Variances")
-    st.write(f"Variance of Set 1: {var_set1:.2f}")
-    st.write(f"Variance of Set 2: {var_set2:.2f}")
-
-    cov_set = np.cov(set1, set2)[0, 1]
-
-    st.subheader("Covariance")
-    st.write(f"Covariance between Set 1 and Set 2: {cov_set:.2f}")
-
-    # Plotting
-    st.subheader("Scatter Plot")
-    st.write("Scatter plot of Set 1 against Set 2")
-    st.write("You can visualize the relationship between the two sets")
-    st.write("Note: This plot requires 'matplotlib' which may not be supported in all Streamlit deployment environments.")
-    
-    # Create the scatter plot
-    fig, ax = plt.subplots()
-    ax.scatter(set1, set2)
-    ax.set_xlabel("Set 1")
-    ax.set_ylabel("Set 2")
-    st.pyplot(fig)
-    
-else:
-    st.write(":red[Please specify the number of data points]")
+    sample_size = st.number_input("Enter the sample size:", min_value=1, step=1, value=100)
+    low = st.number_input("Enter the lower bound of the uniform distribution:", value=0)
+    high = st.number_input("Enter the upper bound of the uniform distribution:", value=1)
+    if st.form_submit_button("Generate Samples"):
+        # Generate random samples
+        samples = np.random.uniform(low, high, sample_size)
+
+    # Display the generated samples
+    if "samples" in locals():
+        st.subheader("Generated Samples")
+        st.write(samples)
+
+        # Calculate and display descriptive statistics
+        st.subheader("Descriptive Statistics")
+        st.write(f"Mean: {np.mean(samples):.2f}")
+        st.write(f"Standard Deviation: {np.std(samples):.2f}")
+        st.write(f"Variance: {np.var(samples):.2f}")
+        st.write(f"Min: {np.min(samples):.2f}")
+        st.write(f"Max: {np.max(samples):.2f}")
+
+        # Visualize the distribution with a histogram
+        st.subheader("Histogram")
+        plt.hist(samples, bins=20, color='skyblue', edgecolor='black')
+        plt.xlabel('Value')
+        plt.ylabel('Frequency')
+        plt.title('Histogram of Random Samples')
+        st.pyplot(plt)
+        plt.close()
+    else:
+        st.warning("Please generate samp