Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,181 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import numpy as np
+import neattext.functions as nfx
+from tensorflow.keras.models import load_model
+import pickle
+from textblob import TextBlob
+import os
+import nltk
+import plotly.graph_objects as go
+
+# --- NLTK PAKETLERİ ---
+try:
+    nltk.data.find('corpora/wordnet')
+except LookupError:
+    nltk.download('wordnet')
+    nltk.download('punkt')
+    nltk.download('omw-1.4')
+
+# --- PICKLE İÇİN GEREKLİ FONKSİYON ---
+# Model eğitilirken kullanılan 'ekkok' fonksiyonu burada tanımlı olmalı
+def ekkok(title):
+    return [word.lemmatize() for word in TextBlob(title).words]
+
+# --- SAYFA AYARLARI ---
+st.set_page_config(page_title="News Classifier", layout="wide")
+
+# --- MODEL VE DOSYALARI YÜKLEME ---
+@st.cache_resource
+def load_assets():
+    model_path = "news_classification_model.h5"
+    vect_path = "tfidf_vectorizer.pkl"
+    le_path = "label_encoder.pkl"
+    
+    # Dosyaların varlığını kontrol et
+    if not all(os.path.exists(p) for p in [model_path, vect_path, le_path]):
+        st.error("⚠️ Model dosyaları bulunamadı! Lütfen .h5 ve .pkl dosyalarını app.py ile aynı klasöre koyun.")
+        st.stop()
+        
+    # Modeli yükle
+    model = load_model(model_path)
+    
+    # Pickle dosyalarını yükle
+    with open(vect_path, "rb") as f:
+        vect = pickle.load(f)
+    with open(le_path, "rb") as f:
+        le = pickle.load(f)
+        
+    return model, vect, le
+
+# Dosyaları belleğe alalım
+model, vect, le = load_assets()
+
+# --- TAHMİN FONKSİYONU ---
+def predict_news(text):
+    # Metin temizleme
+    clean_text = nfx.clean_text(text.lower())
+    # TF-IDF Dönüşümü
+    matrix = vect.transform([clean_text]).toarray()
+    # Model Tahmini
+    prediction = model.predict(matrix, verbose=0)
+    class_index = np.argmax(prediction)
+    prob = np.max(prediction)
+    # Kategori ismini bulma
+    category = le.inverse_transform([class_index])[0]
+    # Sınıf olasılıkları
+    probabilities = prediction[0]
+    return category, prob, probabilities
+
+# --- SIDEBAR (SOL PANEL) - ÇOKLU ÖRNEKLER ---
+st.sidebar.title("📌 Samples / Örnekler")
+
+# Her kategori için 3'er adet örnek cümle (Science/Uzay ağırlıklı)
+all_examples = {
+    "Science / Bilim": [
+        "NASA's Perseverance rover successfully collects high-priority rock samples from the Martian surface.",
+        "The James Webb Space Telescope captures stunning new images of a distant star-forming nebula.",
+        "Astronomers discover a new solar system with three potentially habitable planets."
+    ],
+    "Tech / Teknoloji": [
+        "Apple announces new AI-powered features for the upcoming iPhone 18 Pro series.",
+        "OpenAI releases a new language model that can reason like a human expert.",
+        "Scientists develop a new quantum computer that performs calculations in seconds."
+    ],
+    "Sports / Spor": [
+        "Manchester City secures a narrow victory against Arsenal in a thrilling Premier League match.",
+        "The Olympic Committee announces the final list of cities bidding for the 2032 Games.",
+        "Formula 1 introduces new sustainable fuel regulations to be implemented by 2026."
+    ],
+    "Business / Ekonomi": [
+        "Global stock markets rally as central banks signal potential interest rate cuts.",
+        "The tech industry faces new regulations regarding data privacy and user security.",
+        "Gold prices hit an all-time high amidst global economic uncertainty and inflation."
+    ],
+    "Health / Sağlık": [
+        "New clinical trials show a 90% success rate in a breakthrough cancer treatment.",
+        "Doctors recommend daily exercise and a balanced diet to prevent heart disease.",
+        "A new study reveals the long-term impact of sleep deprivation on mental health."
+    ]
+}
+
+# Kategori seçimi
+selected_cat = st.sidebar.selectbox("Select Category / Kategori Seçin:", [""] + list(all_examples.keys()))
+
+if selected_cat != "":
+    st.sidebar.markdown(f"### {selected_cat} Örnekleri:")
+    st.sidebar.write("Kopyalamak için üzerine tıklayın:")
+    
+    # Seçilen kategorideki 3 örneği de kopyalanabilir code bloğu içinde gösteriyoruz
+    for i, ex in enumerate(all_examples[selected_cat], 1):
+        st.sidebar.info(f"Örnek {i}:")
+        st.sidebar.code(ex, language=None)
+
+# --- ANA SAYFA TASARIMI ---
+st.title("📰 News Topic Categorizer / Haber Sınıflandırıcı")
+st.markdown("---")
+
+# Metin Giriş Alanı (Her zaman boş başlar)
+user_input = st.text_area("News Headline / Haber Başlığı:", height=150, placeholder="Sol taraftan bir örnek kopyalayıp buraya yapıştırın...")
+
+# Tahmin Butonu ve İşlemi
+if st.button("Predict / Tahmin Et"):
+    if user_input.strip() != "":
+        with st.spinner('Analyzing... / Analiz ediliyor...'):
+            # 1. Tahmin Yap
+            category, confidence, all_probs = predict_news(user_input)
+            
+            # 2. Çeviri Yap (TextBlob kullanarak hatasız çeviri, cgi hatası vermez)
+            try:
+                # TextBlob bazen internet bağlantısına göre yavaşlayabilir
+                translated = str(TextBlob(user_input).translate(from_lang='en', to='tr'))
+            except:
+                translated = "Çeviri şu an yapılamıyor. / Translation failed."
+
+            # 3. Efektler
+            st.balloons()
+            
+            # 4. Haber ve Alt Satırda Çeviri (Şık Bilgi Kutusu)
+            st.info(f"**English:** {user_input}\n\n**Türkçe Çeviri:** *{translated}*")
+            
+            st.markdown("---")
+            
+            # 5. Sonuç Kartları (Metric formatında)
+            res_col1, res_col2 = st.columns(2)
+            with res_col1:
+                st.success(f"**Predicted Category / Tahmin Edilen Kategori:**\n\n## {category}")
+            with res_col2:
+                st.warning(f"**Confidence Score / Güven Oranı:**\n\n## %{confidence*100:.2f}")
+
+            # 6. Grafik Ekleme (Plotly ile Şık ve Renkli Görünüm)
+            st.markdown("#### Probability Distribution / Olasılık Dağılımı")
+            
+            # Etiketler ve olasılıklar
+            labels = le.classes_
+            
+            # --- GRAFİK RENGİ MAVİ OLSUN ---
+            fig = go.Figure(go.Bar(
+                x=all_probs, 
+                y=labels, 
+                orientation='h',
+                marker_color='#1F77B4', # Mazarine Blue / Mavi Renk Kodu
+                text=[f"%{p*100:.1f}" for p in all_probs], 
+                textposition='auto'
+            ))
+            
+            fig.update_layout(
+                title="Category Probabilities / Kategori Olasılıkları",
+                xaxis_title="Confidence / Güven",
+                yaxis_title="Categories / Kategoriler",
+                height=400,
+                margin=dict(l=20, r=20, t=40, b=20)
+            )
+            
+            st.plotly_chart(fig, use_container_width=True)
+            
+    else:
+        st.warning("⚠️ Lütfen bir haber başlığı girin! / Please enter a headline!")
 
-"""
-# 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])),
-    ))
+# Footer / Alt Bilgi
+st.markdown("---")
+st.caption("Deep Learning News Classification Project - Powered by Mergen")