uploading libraries

app.py

@@ -1,45 +1,41 @@
 import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+
+
+
 
 # App Title
-st.title("🏋️ Streamlit Fitness Advisor")
-
-# User Input: Age
-age = st.slider("Select your age", min_value=10, max_value=80, value=25)
-
-# User Input: Fitness Goal
-goal = st.selectbox(
-    "What is your main fitness goal?",
-    ["Lose Weight", "Build Muscle", "Increase Endurance", "General Fitness"]
-)
-
-# User Input: Activity Level
-activity_level = st.select_slider(
-    "How active are you?",
-    options=["Sedentary", "Lightly Active", "Moderately Active", "Very Active"],
-    value="Moderately Active"
-)
-
-# Logic for Workout Recommendation
-def get_workout_recommendation(age, goal, activity_level):
-    if goal == "Lose Weight":
-        if activity_level in ["Sedentary", "Lightly Active"]:
-            return "🏃 30-45 minutes of cardio (walking, cycling) + light strength training."
-        else:
-            return "🏋️ HIIT workouts + resistance training 3-4 times per week."
-    
-    elif goal == "Build Muscle":
-        if age < 30:
-            return "💪 Strength training 4-5 times per week + protein-rich diet."
-        else:
-            return "💪 Focus on compound lifts (squats, deadlifts) + adequate recovery."
-    
-    elif goal == "Increase Endurance":
-        return "🏃‍♂️ Running, cycling, or swimming 4-6 times per week with progressive overload."
-    
+st.title("📊 Streamlit Data Analysis Tool")
+
+# Upload CSV File
+uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
+
+if uploaded_file:
+    df = pd.read_csv(uploaded_file)
+    st.write("### Preview of Uploaded Data")
+    st.dataframe(df.head())
+
+    # Dropdown for column selection
+    numeric_cols = df.select_dtypes(include=['number']).columns.tolist()
+    if numeric_cols:
+        col_to_analyze = st.selectbox("Select a column to analyze", numeric_cols)
+
+        # Summary statistics
+        st.write("### Summary Statistics")
+        st.write(df[col_to_analyze].describe())
+
+        # Histogram Slider
+        bins = st.slider("Number of bins for histogram", min_value=5, max_value=50, value=20)
+
+        # Plot Histogram
+        fig, ax = plt.subplots()
+        sns.histplot(df[col_to_analyze], bins=bins, kde=True, ax=ax)
+        st.pyplot(fig)
     else:
-        return "🤸 Balanced mix of cardio, strength training, and flexibility exercises."
+        st.warning("No numeric columns found in the dataset.")
 
-# Display Recommendation
-recommendation = get_workout_recommendation(age, goal, activity_level)
-st.subheader("🏆 Your Personalized Workout Plan:")
-st.write(recommendation)
+else:
+    st.info("Please upload a CSV file to get started.")