Delete streamlit_app.py

streamlit_app.py

@@ -1,67 +0,0 @@
-# To run this app, use: streamlit run test.py
-import streamlit as st
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
-
-# Application title and description
-st.title("Machine Learning Model Visualization")
-st.write("This application demonstrates random forest classification on the iris dataset")
-
-# Data acquisition and preparation
-@st.cache_data
-def load_data():
-    from sklearn.datasets import load_iris
-    iris = load_iris()
-    df = pd.DataFrame(iris.data, columns=iris.feature_names)
-    df['target'] = iris.target
-    return df, iris.target_names
-
-data, target_names = load_data()
-
-# Interactive data exploration
-st.subheader("Dataset Exploration")
-if st.checkbox("Display dataset"):
-    st.dataframe(data)
-
-# Feature selection interface
-st.subheader("Feature Selection")
-features = st.multiselect(
-    "Select features for model training",
-    options=data.columns[:-1],
-    default=data.columns[0]
-)
-
-if len(features) > 0:
-    # Model parameters adjustment
-    st.subheader("Model Parameters")
-    n_estimators = st.slider("Number of trees", 1, 100, 10)
-    max_depth = st.slider("Maximum tree depth", 1, 20, 5)
-    
-    # Model training
-    if st.button("Train Model"):
-        X = data[features]
-        y = data['target']
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
-        
-        model = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=42)
-        model.fit(X_train, y_train)
-        
-        # Performance evaluation
-        y_pred = model.predict(X_test)
-        accuracy = accuracy_score(y_test, y_pred)
-        
-        st.success(f"Model accuracy: {accuracy:.4f}")
-        
-        # Visualization of feature importance
-        if len(features) > 1:
-            st.subheader("Feature Importance")
-            fig, ax = plt.subplots()
-            ax.bar(features, model.feature_importances_)
-            plt.xticks(rotation=45)
-            st.pyplot(fig)
-else:
-    st.warning("Please select at least one feature for model training")