Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, classification_report
+from sklearn.preprocessing import StandardScaler
+
+st.set_page_config(page_title="Random Forest Diabetes Classifier", layout="centered")
+st.title("🌲 Random Forest Classifier - Diabetes Prediction")
+
+uploaded_file = st.file_uploader("📂 Upload your diabetes CSV dataset", type=["csv"])
+
+if uploaded_file:
+    df = pd.read_csv(uploaded_file)
+    st.success("✅ File loaded successfully!")
+    st.write("### Preview of Dataset:")
+    st.dataframe(df.head())
+
+    all_columns = df.columns.tolist()
+
+    target_column = st.selectbox("🎯 Select the target column (diabetes outcome)", all_columns)
+    feature_columns = st.multiselect("🛠️ Select feature columns", [col for col in all_columns if col != target_column])
+
+    if st.button("🚀 Run Random Forest Classifier"):
+        try:
+            X = df[feature_columns]
+            y = df[target_column]
+
+            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+            scaler = StandardScaler()
+            X_train = scaler.fit_transform(X_train)
+            X_test = scaler.transform(X_test)
+
+            model = RandomForestClassifier(n_estimators=100, random_state=42)
+            model.fit(X_train, y_train)
+            y_pred = model.predict(X_test)
+
+            accuracy = accuracy_score(y_test, y_pred)
+            report = classification_report(y_test, y_pred, output_dict=False)
+
+            st.write("### ✅ Accuracy:")
+            st.write(f"{accuracy * 100:.2f}%")
+
+            st.write("### 📋 Classification Report:")
+            st.code(report)
+
+        except Exception as e:
+            st.error(f"❌ An error occurred: {e}")
+else:
+    st.info("👈 Upload a CSV file to begin.")