Spaces:
Sleeping
Sleeping
File size: 5,607 Bytes
2789db4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | import gradio as gr
import sqlite3
import pandas as pd
import plotly.express as px
import random
import json
import os
from datetime import datetime
from transformers import pipeline
# --- DB & MODEL SETUP ---
def init_db():
conn = sqlite3.connect('faithpath.db')
conn.execute('CREATE TABLE IF NOT EXISTS journal_entries (id INTEGER PRIMARY KEY AUTOINCREMENT, date TEXT, entry_text TEXT, detected_emotion TEXT, mission_source TEXT, mission_text TEXT, city TEXT)')
conn.commit(); conn.close()
init_db()
sentiment_analyzer = pipeline('sentiment-analysis', model='savasy/bert-base-turkish-sentiment-cased')
with open('faith_library_final_v56.json', 'r', encoding='utf-8') as f:
faith_library = json.load(f)
def semantic_engine(metin):
t = metin.lower()
logic = {
'öfke_ve_gerginlik': ['nevrim döndü', 'parlamamak', 'zor tutuyorum', 'damarıma bas', 'tepemin tasını'],
'şükran_ve_minnet': ['secde-i şükran', 'hamdolsun', 'minnettarım', 'allah bin bereket'],
'sabır_ve_metanet': ['bağrıma taş bastım', 'isyan etmeksizin', 'dişini sıkıp', 'sabır taşı'],
'cömertlik_ve_paylaşma': ['rızkı paylaştıkça', 'ihtiyacı olana', 'infak etmek', 'sadaka']
}
for cat, words in logic.items():
if any(w in t for w in words): return cat, random.choice(faith_library.get(cat, []))
res = sentiment_analyzer(metin)[0]
cat = 'mutluluk_ve_huzur' if res['label'] == 'positive' else 'hüzün_ve_keder'
return cat, random.choice(faith_library.get(cat, []))
def process_user(text):
if len(text.strip()) < 10: return "<div style='padding:20px; background:#7f1d1d; color:white; border-radius:10px;'>Hata: Analiz için daha detaylı bir cümle yazmalısınız.</div>"
cat, mission = semantic_engine(text)
conn = sqlite3.connect('faithpath.db')
conn.execute('INSERT INTO journal_entries (date, entry_text, detected_emotion, mission_source, mission_text, city) VALUES (?,?,?,?,?,?)',
(datetime.now().strftime('%Y-%m-%d %H:%M:%S'), text, cat, mission['kaynak'], mission['metin'], 'Belirtilmemiş'))
conn.commit(); conn.close()
return f"""<div style='padding:30px; border-radius:20px; background:#0f172a; border:2px solid #10b981; color:white; font-family:sans-serif;'>
<h2 style='color:#10b981; text-align:center; margin-top:0;'>🕊️ Manevi Rehberlik</h2>
<div style='background:#1e293b; padding:20px; border-radius:15px; margin:20px 0; border-left:6px solid #3b82f6;'>
<p style='color:#94a3b8; font-size:0.8em; text-transform:uppercase; margin-bottom:5px;'>Sistem Analizi: {cat.replace('_',' ').title()}</p>
<p style='font-size:1.3em; font-style:italic; line-height:1.5;'>"{mission['metin']}"</p>
<p style='text-align:right; color:#10b981; font-weight:bold;'>— {mission['kaynak']}</p>
</div>
<div style='background:#064e3b; padding:20px; border-radius:15px; border-left:6px solid #fbbf24;'>
<strong style='color:#fbbf24; display:block; margin-bottom:8px; font-size:1.1em;'>🎯 Bugünün Kalp Görevi:</strong>
<p style='font-size:1.1em; line-height:1.4;'>{mission.get('rehber_mesaji', 'Ailenizle vakit geçirin.')}</p>
</div>
</div>"""
def get_admin(pwd):
if pwd != 'Enes.13112006': return gr.update(visible=False), None, None, "### Hata: Yetkisiz Erişim"
conn = sqlite3.connect('faithpath.db'); df = pd.read_sql_query('SELECT * FROM journal_entries ORDER BY id DESC', conn); conn.close()
if df.empty: return gr.update(visible=True), None, None, "Veri yok."
# Plot 1: Sentiment Distribution
fig_pie = px.pie(df, names='detected_emotion', title='Genel Duygu Dağılımı', template='plotly_dark', hole=.4)
# Plot 2: Time Trend (DD/MM/YYYY)
df['date_dt'] = pd.to_datetime(df['date'])
trend_df = df.groupby(df['date_dt'].dt.date).size().reset_index(name='Sayi')
fig_line = px.line(trend_df, x='date_dt', y='Sayi', title='Kayıt Trendi', template='plotly_dark', markers=True)
fig_line.update_xaxes(tickformat="%d/%m/%Y")
table = df[['date', 'entry_text', 'detected_emotion']].to_html(classes='table table-dark table-hover', index=False)
return gr.update(visible=True), fig_pie, fig_line, f"### Toplam Kayıt: {len(df)}\n{table}"
# --- UI DESIGN ---
with gr.Blocks(theme=gr.themes.Soft(), css='.gradio-container {background-color: #0f172a}') as demo:
gr.Markdown("<h1 style='text-align:center; color:#10b981;'>🕊️ FaithPath Professional v5.8.8</h1>")
with gr.Tabs():
with gr.Tab('🕊️ Rehberlik Asistanı'):
with gr.Row():
with gr.Column(scale=2):
txt = gr.Textbox(label='Bugün ailenizle ilgili neler yaşadınız?', placeholder='Duygularınızı buraya yazın...', lines=10)
btn = gr.Button('Analiz Et ve Rehberlik Al', variant='primary')
with gr.Column(scale=3): out = gr.HTML()
btn.click(process_user, [txt], out)
with gr.Tab('📊 Yönetici Paneli'):
with gr.Row():
pwd = gr.Textbox(label='Yönetici Şifresi', type='password'); lbtn = gr.Button('Sisteme Giriş')
with gr.Column(visible=False) as zone:
with gr.Row():
plt1 = gr.Plot(); plt2 = gr.Plot()
stats = gr.HTML()
lbtn.click(get_admin, pwd, [zone, plt1, plt2, stats])
gr.Markdown("<center style='color:#64748b; margin-top:20px;'>Enes YILMAZ - Karabük University | Time Analysis & Simplified UI</center>")
demo.launch() |