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fa5a05b
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  1. app.py +109 -0
  2. requirements.txt +3 -0
app.py ADDED
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+ import streamlit as st
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+ import re
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+ import numpy as np
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+ import matplotlib.pyplot as plt
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+ from io import BytesIO
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+
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+ # Başlık
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+ st.title("Note Analyzer Streamlit Uygulaması")
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+
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+ # Kullanıcıdan veri alma
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+ st.sidebar.header("Girdi Alanları")
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+ uploaded_file = st.sidebar.file_uploader("Notlar Dosyasını Yükleyin (TXT)", type=["txt"])
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+ lecture_name = st.sidebar.text_input("Ders Adı", value="Ders Adı")
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+ perfect_score = st.sidebar.number_input("Sınav Puanı Üst Limiti", value=100, step=1)
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+ my_note = st.sidebar.number_input("Benim Notum", value=0.0, step=0.1)
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+ note_s_axis_diff = st.sidebar.number_input("Notlar X Ekseni Ortak Farkı", value=5, step=1)
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+ amount_s_axis_diff = st.sidebar.number_input("Miktar Y Ekseni Ortak Farkı", value=1, step=1)
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+ first_step = st.sidebar.number_input("İlk Adım", value=0, step=1)
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+ increase_amount = st.sidebar.number_input("Artış Miktarı", value=1, step=1)
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+
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+ if st.sidebar.button("Analizi Çalıştır"):
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+ if uploaded_file is None:
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+ st.error("Lütfen bir dosya yükleyin!")
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+ else:
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+ try:
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+ # Dosya içeriğini okuma
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+ content = uploaded_file.read().decode("utf-8")
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+ result = re.split(r'[ \n]+', content)
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+
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+ # Veriyi filtreleme ve işleme
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+ notes_result = result[first_step::increase_amount]
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+ notes_result = [x for x in notes_result if x != '∅' and x != "NA"]
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+ notes_result = list(map(lambda x: float(x), notes_result))
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+ notes_result = np.array(notes_result)
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+
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+ # İstatistikler
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+ average_x = np.average(notes_result)
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+ min_x = notes_result.min()
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+ max_x = notes_result.max()
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+ std = np.std(notes_result)
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+ z_score = (my_note - average_x) / std
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+
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+ # İstatistikleri ekrana yazdırma
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+ st.subheader("Genel Bilgiler")
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+ st.write(f"Katilimci Sayısı: {len(notes_result)}")
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+ st.write(f"En Düşük Not: {min_x:.2f}")
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+ st.write(f"En Yüksek Not: {max_x:.2f}")
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+ st.write(f"Ortalama Not: {average_x:.2f}")
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+ st.write(f"Standart Sapma: {std:.2f}")
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+ st.write(f"Z-Skoru: {z_score:.2f}")
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+
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+ # Grafik oluşturma
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+ st.subheader("Not Dağılım Grafiği")
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+ unique_values, counts = np.unique(notes_result, return_counts=True)
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+ plt.figure(figsize=(10, 6))
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+ bars = plt.bar(unique_values, counts, width=0.3)
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+ plt.axvline(x=average_x, color='red', linestyle='--')
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+ plt.text(average_x + 1.5, max(counts), 'Ortalama Not', color='red', rotation=0, ha='center', va='bottom')
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+
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+ if my_note in unique_values:
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+ plt.text(my_note, counts[unique_values == my_note][0], 'Benim\nNotum', color='green', rotation=0, ha='center', va='bottom')
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+
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+ for bar in bars:
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+ if bar.get_x() <= my_note < bar.get_x() + bar.get_width():
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+ bar.set_color('green')
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+
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+ plt.title(f'{lecture_name} Not Sayıları Grafiği')
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+ plt.xlabel('Notlar')
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+ plt.ylabel('Adet')
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+ plt.xticks(range(0, int(perfect_score), note_s_axis_diff), rotation=90)
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+ plt.yticks(range(0, max(counts), amount_s_axis_diff), rotation=0)
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+
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+ # Grafik bilgileri
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+ info_text = (
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+ f"Katilimci sayısı: {len(notes_result)}\n"
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+ f"En düşük not: {min_x:.2f}\n"
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+ f"En yüksek not: {max_x:.2f}\n"
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+ f"Benim notum: {my_note:.2f}\n"
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+ f"Ortalama not: {average_x:.2f}\n"
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+ f"Standart sapma: {std:.2f}\n"
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+ f"Z-skoru: {z_score:.2f}"
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+ )
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+ plt.text(
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+ 1.05 * max(unique_values), 0.8 * max(counts),
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+ info_text,
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+ fontsize=10,
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+ color="black",
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+ ha="left",
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+ va="top",
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+ bbox=dict(boxstyle="round,pad=0.3", edgecolor="blue", facecolor="lightgrey")
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+ )
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+ plt.subplots_adjust(left=0.055, bottom=0.065, right=0.90, top=0.962, wspace=0.2, hspace=0.2)
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+
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+ # Grafik gösterimi
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+ st.pyplot(plt)
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+
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+ # Grafik indirme bağlantısı
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+ buf = BytesIO()
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+ plt.savefig(buf, format="png")
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+ buf.seek(0)
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+ st.download_button(
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+ label="Grafiği İndir",
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+ data=buf,
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+ file_name="not_dagilimi.png",
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+ mime="image/png"
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+ )
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+
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+ except Exception as e:
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+ st.error(f"Hata: {e}")
requirements.txt ADDED
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+ streamlit
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+ matplotlib
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+ numpy