modified code

app.py

@@ -9,67 +9,60 @@ import os
 # Fix protobuf compatibility issue
 os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
 
-# Sidebar Navigation
-st.sidebar.title("Iris Flower Classification")
-page = st.sidebar.radio("Navigation", ["Overview", "Dataset Upload", "Dataset Details", "Data Visualization", "Prediction"])
+# Load Dataset
+df = pd.read_csv("Iris.csv")
+df.rename(columns={'Species': 'species'}, inplace=True)
 
-df = None
-if page == "Dataset Upload":
-    st.title("Upload Dataset")
-    uploaded_file = st.file_uploader("Upload your Iris dataset (CSV format)", type=["csv"])
-    if uploaded_file is not None:
-        df = pd.read_csv(uploaded_file)
-        df.rename(columns={'Species': 'species'}, inplace=True)
-        st.success("Dataset successfully uploaded!")
+# Tabs Navigation
+tabs = st.tabs(["Overview", "Dataset Details", "Data Visualization", "Prediction"])
 
-if df is not None:
-    if page == "Overview":
-        st.title("Overview")
-        st.write("""
-        This app uses machine learning to classify Iris flowers into three species:
-        - Setosa
-        - Versicolor
-        - Virginica
-        
-        The model is trained using the **Random Forest Classifier**.
-        """)
+with tabs[0]:  # Overview
+    st.title("Overview")
+    st.write("""
+    This app uses machine learning to classify Iris flowers into three species:
+    - Setosa
+    - Versicolor
+    - Virginica
     
-    elif page == "Dataset Details":
-        st.title("Dataset Details")
-        st.write("### Sample Data")
-        st.dataframe(df.head())
-        st.write("### Dataset Statistics")
-        st.write(df.describe())
+    The model is trained using the **Random Forest Classifier**.
+    """)
+
+with tabs[1]:  # Dataset Details
+    st.title("Dataset Details")
+    st.write("### Sample Data")
+    st.dataframe(df.head())
+    st.write("### Dataset Statistics")
+    st.write(df.describe())
+
+with tabs[2]:  # Data Visualization
+    st.title("Data Visualization")
+    st.write("### Pairplot of Features")
+    fig = sns.pairplot(df, hue="species", diag_kind="kde")
+    st.pyplot(fig)
+    
+    st.write("### Feature Correlation")
+    fig, ax = plt.subplots()
+    sns.heatmap(df.corr(), annot=True, cmap='coolwarm', ax=ax)
+    st.pyplot(fig)
+
+with tabs[3]:  # Prediction
+    st.title("Iris Flower Prediction")
+    
+    st.sidebar.header("Enter flower measurements:")
+    sepal_length = st.sidebar.slider("Sepal Length (cm)", 4.0, 8.0, 5.0)
+    sepal_width = st.sidebar.slider("Sepal Width (cm)", 2.0, 5.0, 3.0)
+    petal_length = st.sidebar.slider("Petal Length (cm)", 1.0, 7.0, 4.0)
+    petal_width = st.sidebar.slider("Petal Width (cm)", 0.1, 2.5, 1.0)
+    
+    X = df.iloc[:, 1:-1]
+    y = df['species']
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    model = RandomForestClassifier()
+    model.fit(X_train, y_train)
     
-    elif page == "Data Visualization":
-        st.title("Data Visualization")
-        st.write("### Pairplot of Features")
-        fig = sns.pairplot(df, hue="species", diag_kind="kde")
-        st.pyplot(fig)
-        
-        st.write("### Feature Correlation")
-        fig, ax = plt.subplots()
-        sns.heatmap(df.corr(), annot=True, cmap='coolwarm', ax=ax)
-        st.pyplot(fig)
+    input_data = [[sepal_length, sepal_width, petal_length, petal_width]]
+    prediction = model.predict(input_data)[0]
     
-    elif page == "Prediction":
-        st.title("Iris Flower Prediction")
-        
-        st.sidebar.header("Enter flower measurements:")
-        sepal_length = st.sidebar.slider("Sepal Length (cm)", 4.0, 8.0, 5.0)
-        sepal_width = st.sidebar.slider("Sepal Width (cm)", 2.0, 5.0, 3.0)
-        petal_length = st.sidebar.slider("Petal Length (cm)", 1.0, 7.0, 4.0)
-        petal_width = st.sidebar.slider("Petal Width (cm)", 0.1, 2.5, 1.0)
-        
-        X = df.iloc[:, 1:-1]
-        y = df['species']
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-        
-        model = RandomForestClassifier()
-        model.fit(X_train, y_train)
-        
-        input_data = [[sepal_length, sepal_width, petal_length, petal_width]]
-        prediction = model.predict(input_data)[0]
-        
-        st.write("### Prediction Result")
-        st.success(f"The predicted species is: {prediction}")
+    st.write("### Prediction Result")
+    st.success(f"The predicted species is: {prediction}")