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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import accuracy_score
+
+# Iris veri setini yükle
+iris = load_iris()
+df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
+df['species'] = iris.target
+
+# Hedef etiketleri göster
+st.title("Iris Çiçeği Türü Tahmin Uygulaması")
+st.write("Hedef Etiketler: ", df["species"].unique())
+
+# Özellikleri ve etiketleri ayır
+X = df[iris.feature_names]
+y = df['species']
+
+# Veriyi eğitim ve test setlerine ayır
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Modeli oluştur ve eğit
+model = LogisticRegression(max_iter=200)
+model.fit(X_train, y_train)
+
+# Kullanıcıdan girdi al
+st.sidebar.header("Girdi Verileri")
+sepal_length = st.sidebar.number_input("Sepal Uzunluğu (cm)", min_value=4.0, max_value=8.0, value=5.0)
+sepal_width = st.sidebar.number_input("Sepal Genişliği (cm)", min_value=2.0, max_value=5.0, value=3.0)
+petal_length = st.sidebar.number_input("Petal Uzunluğu (cm)", min_value=1.0, max_value=7.0, value=1.5)
+petal_width = st.sidebar.number_input("Petal Genişliği (cm)", min_value=0.1, max_value=2.5, value=0.2)
+
+# Tahmin yapma
+input_data = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
+if st.button("Tahmin Et"):
+    prediction = model.predict(input_data)
+    predicted_species = iris.target_names[prediction][0]
+
+    # Tahmin sonucunu göster
+    st.subheader("Tahmin Edilen Tür:")
+    st.write(predicted_species)

requirements.txt

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+streamlit
+tensorflow
+opencv-python
+scikit-learn