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import streamlit as st
import re
import numpy as np
import matplotlib.pyplot as plt
from io import BytesIO
# Başlık
st.title("Note Analyzer Streamlit Uygulaması")
# Kullanıcıdan veri alma
st.sidebar.header("Girdi Alanları")
uploaded_file = st.sidebar.file_uploader("Notlar Dosyasını Yükleyin (TXT)", type=["txt"])
lecture_name = st.sidebar.text_input("Ders Adı", value="Ders Adı")
perfect_score = st.sidebar.number_input("Sınav Puanı Üst Limiti", value=100, step=1)
my_note = st.sidebar.number_input("Benim Notum", value=0.0, step=0.1)
note_s_axis_diff = st.sidebar.number_input("Notlar X Ekseni Ortak Farkı", value=5, step=1)
amount_s_axis_diff = st.sidebar.number_input("Miktar Y Ekseni Ortak Farkı", value=1, step=1)
first_step = st.sidebar.number_input("İlk Adım", value=0, step=1)
increase_amount = st.sidebar.number_input("Artış Miktarı", value=1, step=1)
if st.sidebar.button("Analizi Çalıştır"):
if uploaded_file is None:
st.error("Lütfen bir dosya yükleyin!")
else:
try:
# Dosya içeriğini okuma
content = uploaded_file.read().decode("utf-8")
result = re.split(r'[ \n]+', content)
# Veriyi filtreleme ve işleme
notes_result = result[first_step::increase_amount]
notes_result = [x for x in notes_result if x != '∅' and x != "NA"]
notes_result = list(map(lambda x: float(x), notes_result))
notes_result = np.array(notes_result)
# İstatistikler
average_x = np.average(notes_result)
min_x = notes_result.min()
max_x = notes_result.max()
std = np.std(notes_result)
z_score = (my_note - average_x) / std
# İstatistikleri ekrana yazdırma
st.subheader("Genel Bilgiler")
st.write(f"Katilimci Sayısı: {len(notes_result)}")
st.write(f"En Düşük Not: {min_x:.2f}")
st.write(f"En Yüksek Not: {max_x:.2f}")
st.write(f"Ortalama Not: {average_x:.2f}")
st.write(f"Standart Sapma: {std:.2f}")
st.write(f"Z-Skoru: {z_score:.2f}")
# Grafik oluşturma
st.subheader("Not Dağılım Grafiği")
unique_values, counts = np.unique(notes_result, return_counts=True)
plt.figure(figsize=(10, 6))
bars = plt.bar(unique_values, counts, width=0.3)
plt.axvline(x=average_x, color='red', linestyle='--')
plt.text(average_x + 1.5, max(counts), 'Ortalama Not', color='red', rotation=0, ha='center', va='bottom')
if my_note in unique_values:
plt.text(my_note, counts[unique_values == my_note][0], 'Benim\nNotum', color='green', rotation=0, ha='center', va='bottom')
for bar in bars:
if bar.get_x() <= my_note < bar.get_x() + bar.get_width():
bar.set_color('green')
plt.title(f'{lecture_name} Not Sayıları Grafiği')
plt.xlabel('Notlar')
plt.ylabel('Adet')
plt.xticks(range(0, int(perfect_score), note_s_axis_diff), rotation=90)
plt.yticks(range(0, max(counts), amount_s_axis_diff), rotation=0)
# Grafik bilgileri
info_text = (
f"Katilimci sayısı: {len(notes_result)}\n"
f"En düşük not: {min_x:.2f}\n"
f"En yüksek not: {max_x:.2f}\n"
f"Benim notum: {my_note:.2f}\n"
f"Ortalama not: {average_x:.2f}\n"
f"Standart sapma: {std:.2f}\n"
f"Z-skoru: {z_score:.2f}"
)
plt.text(
1.05 * max(unique_values), 0.8 * max(counts),
info_text,
fontsize=10,
color="black",
ha="left",
va="top",
bbox=dict(boxstyle="round,pad=0.3", edgecolor="blue", facecolor="lightgrey")
)
plt.subplots_adjust(left=0.055, bottom=0.065, right=0.90, top=0.962, wspace=0.2, hspace=0.2)
# Grafik gösterimi
st.pyplot(plt)
# Grafik indirme bağlantısı
buf = BytesIO()
plt.savefig(buf, format="png")
buf.seek(0)
st.download_button(
label="Grafiği İndir",
data=buf,
file_name="not_dagilimi.png",
mime="image/png"
)
except Exception as e:
st.error(f"Hata: {e}") |