Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,41 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pickle
+import numpy as np
+
+st.title('Kişilik Özelliklerine Göre Tahmin Modeli :bust_in_silhouette:')
+
+with open('behv.pkl', 'rb') as file:
+    model, feature_names = pickle.load(file)
+
+st.header("Aşağıdaki alanları doldurun:")
+
+inputs = []
+inputs.append(st.number_input('Time spent Alone (Saat)', min_value=0.0, max_value=24.0, value=1.0, step=1.0))
+inputs.append(st.number_input('Stage fear (1=Evet, 0=Hayır)', min_value=0.0, max_value=1.0, value=0.0, step=1.0))
+inputs.append(st.number_input('Social event attendance', min_value=0.0, max_value=10.0, value=1.0, step=1.0))
+inputs.append(st.number_input('Going outside', min_value=0.0, max_value=10.0, value=1.0, step=1.0))
+inputs.append(st.number_input('Drained after socializing (1=Evet, 0=Hayır)', min_value=0.0, max_value=1.0, value=0.0, step=1.0))
+inputs.append(st.number_input('Friends circle size', min_value=0.0, max_value=20.0, value=1.0, step=1.0))
+inputs.append(st.number_input('Post frequency', min_value=0.0, max_value=10.0, value=1.0, step=1.0))
+
+if st.button('Tahmin Et'):
+    input_array = np.array([inputs])
+    prediction = model.predict(input_array)
+    # Eğer çıktı [ [sayı] ] gibi ise aç
+    if hasattr(prediction, '__len__'):
+        output = prediction[0]
+        if hasattr(output, '__len__'):
+            output = output[0]
+    else:
+        output = prediction
+
+    # 0: Introvert, 1: Extrovert
+    label_map = {0: "Introvert", 1: "Extrovert"}
+    try:
+        class_idx = int(round(output))
+        result = label_map.get(class_idx, f"Bilinmeyen sınıf ({output})")
+    except Exception:
+        result = f"Bilinmeyen sonuç: {output}"
+    st.success(f"Tahmin sonucu: {result}")
+
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))