Update app.py

app.py

@@ -11,9 +11,6 @@ import matplotlib.pyplot as plt
 import re
 import html
 import requests
-import torch
-import spaces # ZeroGPU için gerekli kütüphane
-
 from sentence_transformers import SentenceTransformer
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline, pipeline
 import warnings
@@ -35,12 +32,18 @@ TRANSLATOR_NAME = "Helsinki-NLP/opus-mt-tc-big-en-tr"
 # --- Ayarlar ---
 NEUTRAL_CONFIDENCE_FLOOR = 0.55
 
-# --- Sinyal Kelimeleri (Aynı) ---
+# --- Sinyal Kelimeleri ---
 NEGATIVE_CUES = { "crash", "crashes", "dump", "dumps", "plunge", "plunges", "tumble", "tumbles", "falls", "fall", "drop", "drops", "slump", "slumps", "sell-off", "selloff", "panic", "fear", "fears", "concern", "concerns", "pressure", "pressures", "risk", "risks", "risk-off", "lawsuit", "hacked", "hack", "breach", "ban", "banned", "crackdown", "probe", "investigation", "charges", "liquidation", "liquidations", "lag", "lags", "weak", "weaker", "over?", "collapse", "collapses", "recession", "loss", "losses" }
 POSITIVE_CUES = { "surge", "surges", "pump", "pumps", "rally", "rallies", "soar", "soars", "breakout", "breaks out", "record", "ath", "all-time high", "wins", "approval", "approved", "etf", "inflows", "adoption", "partnership", "partners", "launch", "launches", "upgrade", "upgrades", "bull", "bullish", "rise", "rises", "beats", "rebound", "rebounds", "gain", "gains", "bullrun" }
-LABEL_MAP = { "bullish": "OLUMLU", "bearish": "OLUMSUZ", "neutral": "NÖTR" }
 
-# --- Yardımcı Fonksiyonlar ---
+# --- Çeviri Haritası (Görünüm İçin) ---
+LABEL_MAP = {
+    "bullish": "OLUMLU",
+    "bearish": "OLUMSUZ",
+    "neutral": "NÖTR"
+}
+
+# --- TEMİZLEME VE YARDIMCI FONKSİYONLAR ---
 def clean_html_tags(text):
     if not text: return ""
     text = html.unescape(text)
@@ -53,6 +56,31 @@ def clean_html_tags(text):
     text = re.sub(r'\s+', ' ', text).strip()
     return text
 
+def initialize_models():
+    global model, sentiment_analyzer, translator
+    status_msg = []
+    try:
+        if model is None:
+            model = SentenceTransformer('all-MiniLM-L6-v2')
+            status_msg.append("✅ Embedding Modeli Hazır")
+    except Exception as e: status_msg.append(f"❌ Embedding: {str(e)}")
+
+    try:
+        if sentiment_analyzer is None:
+            tokenizer = AutoTokenizer.from_pretrained(CRYPTOBERT_NAME, use_fast=True)
+            clf_model = AutoModelForSequenceClassification.from_pretrained(CRYPTOBERT_NAME)
+            sentiment_analyzer = TextClassificationPipeline(model=clf_model, tokenizer=tokenizer, device=-1, max_length=128, truncation=True, padding="max_length")
+            status_msg.append("✅ Sentiment Modeli Hazır")
+    except Exception as e: status_msg.append(f"❌ Sentiment: {str(e)}")
+
+    try:
+        if translator is None:
+            translator = pipeline("translation", model=TRANSLATOR_NAME, device=-1)
+            status_msg.append("✅ Çeviri Modeli Hazır")
+    except Exception as e: status_msg.append(f"❌ Çeviri: {str(e)}")
+    
+    return " | ".join(status_msg)
+
 def _normalize_label(label: str) -> str:
     l = (label or "").strip().lower()
     if l in {"label_2"} or "bull" in l: return "bullish"
@@ -86,49 +114,51 @@ def fetch_feed_data(url):
         return feedparser.parse(response.content) if response.status_code == 200 else None
     except: return None
 
-# --- GPU İŞLEMLERİ ---
-
-# Modelleri Yükle (GPU Ayarlı)
-@spaces.GPU  # Modelleri yüklerken de GPU context'i olsun
-def initialize_models():
-    global model, sentiment_analyzer, translator
-    status_msg = []
-    
-    # GPU var mı kontrolü (ZeroGPU'da her zaman vardır ama kod güvenliği için)
-    device = 0 if torch.cuda.is_available() else -1
+# --- HTML FORMATLAMA FONKSİYONU ---
+def format_news_as_html(dataframe):
+    if dataframe is None or len(dataframe) == 0:
+        return "<div style='padding:20px; text-align:center; color: #666;'>Haber yok. Lütfen 'Verileri Yenile' butonuna basın.</div>"
     
-    try:
-        if model is None:
-            # SentenceTransformer otomatik olarak GPU kullanır varsa
-            model = SentenceTransformer('all-MiniLM-L6-v2')
-            status_msg.append("✅ Embedding Modeli Hazır")
-    except Exception as e: status_msg.append(f"❌ Embedding: {str(e)}")
-
-    try:
-        if sentiment_analyzer is None:
-            tokenizer = AutoTokenizer.from_pretrained(CRYPTOBERT_NAME, use_fast=True)
-            clf_model = AutoModelForSequenceClassification.from_pretrained(CRYPTOBERT_NAME)
-            # device=0 diyerek GPU'ya zorluyoruz
-            sentiment_analyzer = TextClassificationPipeline(model=clf_model, tokenizer=tokenizer, device=device, max_length=128, truncation=True, padding="max_length")
-            status_msg.append("✅ Sentiment Modeli Hazır")
-    except Exception as e: status_msg.append(f"❌ Sentiment: {str(e)}")
-
-    try:
-        if translator is None:
-            # device=0 diyerek GPU'ya zorluyoruz
-            translator = pipeline("translation", model=TRANSLATOR_NAME, device=device)
-            status_msg.append("✅ Çeviri Modeli Hazır")
-    except Exception as e: status_msg.append(f"❌ Çeviri: {str(e)}")
+    html_content = "<div class='news-feed-container'>"
     
-    return " | ".join(status_msg)
+    for _, row in dataframe.iterrows():
+        sentiment = row['sentiment_label']
+        score = row['sentiment_score']
+        
+        # Skor Formatı: 0.98 -> 98
+        confidence_percent = int(score * 100)
+        
+        # Türkçe Etiket
+        tr_label = LABEL_MAP.get(sentiment, "NÖTR")
+        
+        # Renk sınıfları
+        color_class = "neutral-card"
+        icon = "➖"
+        if sentiment == "bullish": 
+            color_class = "bullish-card"
+            icon = "🚀"
+        elif sentiment == "bearish": 
+            color_class = "bearish-card"
+            icon = "🔻"
+            
+        html_content += f"""
+        <div class='news-card {color_class}'>
+            <div class='card-header'>
+                <span class='badge {sentiment}'>{icon} {tr_label} (%{confidence_percent})</span>
+                <span class='date'>{row['published'][:16]}</span>
+            </div>
+            <h3><a href="{row['link']}" target="_blank">{row['title']}</a></h3>
+            <p>{row['summary'][:160]}...</p>
+        </div>
+        """
+    html_content += "</div>"
+    return html_content
 
-# Haber Çekme ve Analiz (GPU kullanır)
-@spaces.GPU(duration=60) # Bu işlem biraz sürebilir, süre tanıyalım
 def fetch_news_wrapper():
     global df, index, embeddings, sentiment_analyzer, model
     
     if sentiment_analyzer is None:
-        initialize_models() # Eğer yüklenmediyse yükle
+        return "⚠️ Önce Modelleri Yükleyin!", "", gr.update(choices=[]), None
         
     RSS_URLS = [
         "https://cointelegraph.com/rss",
@@ -142,7 +172,7 @@ def fetch_news_wrapper():
     for url in RSS_URLS:
         feed = fetch_feed_data(url)
         if feed and feed.entries:
-            for entry in feed.entries[:25]: # GPU olduğu için sayıyı biraz artırabiliriz
+            for entry in feed.entries[:20]:
                 clean_title = clean_html_tags(entry.get("title", ""))
                 if clean_title:
                     all_entries.append({
@@ -157,70 +187,62 @@ def fetch_news_wrapper():
         
     df = pd.DataFrame(all_entries).drop_duplicates(subset="title").reset_index(drop=True)
     
-    # Sentiment Analizi Döngüsü
-    sentiments = []
-    scores = []
-    
-    # Toplu işleme gerek yok, GPU hızlıdır, loop yeterli
-    for _, row in df.iterrows():
+    # Sentiment Analizi
+    def analyze_row(row):
         text = f"{row['title']}. {row['summary']}"[:1000]
         try:
             out = sentiment_analyzer(text)[0]
             lbl, scr = _apply_post_rules(_normalize_label(out.get("label")), float(out.get("score")), text)
-            sentiments.append(lbl)
-            scores.append(scr)
-        except:
-            sentiments.append("neutral")
-            scores.append(0.0)
+            return lbl, scr
+        except: return "neutral", 0.0
 
-    df["sentiment_label"] = sentiments
-    df["sentiment_score"] = scores
+    df["sentiment_label"], df["sentiment_score"] = zip(*df.apply(analyze_row, axis=1))
     
-    # Faiss & Embeddings
+    # Faiss
     corpus = df['title'].tolist()
     embeddings = model.encode(corpus, show_progress_bar=False)
     index = faiss.IndexFlatL2(embeddings.shape[1])
     index.add(embeddings.astype('float32'))
     
-    status_text = f"✅ {len(df)} Haber Analiz Edildi (GPU Hızlandırmalı ⚡)"
+    # UI Çıktıları
+    status_text = f"✅ {len(df)} Haber Analiz Edildi"
     html_feed = format_news_as_html(df)
     choices = [(f"{i}. {t[:40]}...", i) for i, t in enumerate(df["title"])]
     
-    # Grafik
+    # Grafik oluştur (Türkçe Etiketli)
     sentiment_counts = df["sentiment_label"].value_counts()
+    
     fig, ax = plt.subplots(figsize=(6, 3))
     colors = {'bullish': '#4CAF50', 'bearish': '#F44336', 'neutral': '#FFC107'}
+    
+    # Etiketleri Türkçeye çevir
     tr_labels = [LABEL_MAP.get(x, x) for x in sentiment_counts.index]
     bar_colors = [colors.get(x, '#333') for x in sentiment_counts.index]
+    
     ax.bar(tr_labels, sentiment_counts.values, color=bar_colors)
     ax.set_title("Piyasa Duygu Durumu")
     plt.tight_layout()
     
     return status_text, html_feed, gr.update(choices=choices, value=None), fig
 
-# Çeviri (GPU Kesin Gerekli)
-@spaces.GPU
+# --- Çeviri ve Arama Fonksiyonları ---
 def perform_translation(news_index):
-    global df, translator
-    if translator is None: initialize_models()
-    
+    global df
     if df is None or news_index is None: return "Lütfen bir haber seçin.", "..."
     try:
         idx = int(news_index)
         row = df.iloc[idx]
-        
-        # GPU'da çeviri çok hızlıdır
-        title_tr = translator(row['title'][:512])[0]['translation_text']
-        
-        summ = row['summary']
-        if not summ: summ = "Özet yok."
-        summary_tr = translator(summ[:512])[0]['translation_text']
-        
+        title_tr = translate_text_en_to_tr(row['title'])
+        summary_tr = translate_text_en_to_tr(row['summary'])
         return title_tr, summary_tr
     except Exception as e: return f"Hata: {e}", "..."
 
-# Arama (CPU yeterli ama GPU varsa kullansın)
-@spaces.GPU
+def translate_text_en_to_tr(text):
+    global translator
+    if not text: return ""
+    try: return translator(text[:512])[0]['translation_text']
+    except: return "Çeviri hatası"
+
 def search_news(query):
     global df, index, model
     if df is None: return "Veri yok."
@@ -232,13 +254,14 @@ def search_news(query):
     except: return "Arama hatası."
 
 def analyze_coin(coin_name):
-    # Bu sadece filtreleme yaptığı için GPU'ya gerek yok
     global df
     if df is None: return None, "Veri yok."
     filtered = df[df["title"].str.contains(coin_name, case=False, na=False)]
     if len(filtered) == 0: return None, f"{coin_name} hakkında haber yok."
     
     counts = filtered["sentiment_label"].value_counts()
+    
+    # Türkçe etiketler ve renkler
     labels_tr = [LABEL_MAP.get(x, x) for x in counts.index]
     colors = ['#4CAF50' if x=='bullish' else '#F44336' if x=='bearish' else '#FFC107' for x in counts.index]
     
@@ -247,97 +270,126 @@ def analyze_coin(coin_name):
     ax.set_title(f"{coin_name.upper()} Analizi")
     return fig, format_news_as_html(filtered)
 
-# HTML Formatlama (Görsel İşler)
-def format_news_as_html(dataframe):
-    if dataframe is None or len(dataframe) == 0:
-        return "<div style='padding:20px; text-align:center; color: #666;'>Haber yok. Lütfen 'Verileri Yenile' butonuna basın.</div>"
-    
-    html_content = "<div class='news-feed-container'>"
-    for _, row in dataframe.iterrows():
-        sentiment = row['sentiment_label']
-        score = row['sentiment_score']
-        confidence_percent = int(score * 100)
-        tr_label = LABEL_MAP.get(sentiment, "NÖTR")
-        
-        color_class = "neutral-card"
-        icon = "➖"
-        if sentiment == "bullish": 
-            color_class = "bullish-card"
-            icon = "🚀"
-        elif sentiment == "bearish": 
-            color_class = "bearish-card"
-            icon = "🔻"
-            
-        html_content += f"""
-        <div class='news-card {color_class}'>
-            <div class='card-header'>
-                <span class='badge {sentiment}'>{icon} {tr_label} (%{confidence_percent})</span>
-                <span class='date'>{row['published'][:16]}</span>
-            </div>
-            <h3><a href="{row['link']}" target="_blank">{row['title']}</a></h3>
-            <p>{row['summary'][:160]}...</p>
-        </div>
-        """
-    html_content += "</div>"
-    return html_content
-
-# --- CSS ---
+# --- CSS STİLLERİ ---
 custom_css = """
-.news-card { background-color: #ffffff; border-radius: 10px; padding: 15px; margin-bottom: 15px; border: 1px solid #e0e0e0; box-shadow: 0 2px 5px rgba(0,0,0,0.05); }
-.news-card h3 a { text-decoration: none; color: #222222 !important; font-weight: 700; }
-.news-card p { color: #444444 !important; font-size: 0.95em; line-height: 1.5; }
-.card-header { display: flex; justify-content: space-between; margin-bottom: 8px; }
-.date { font-size: 0.8em; color: #888888 !important; }
+/* Genel Kart Yapısı */
+.news-card {
+    background-color: #ffffff; 
+    border-radius: 10px;
+    padding: 15px;
+    margin-bottom: 15px;
+    border: 1px solid #e0e0e0;
+    box-shadow: 0 2px 5px rgba(0,0,0,0.05);
+}
+
+/* Yazı renklerini zorla koyu yap */
+.news-card h3 a {
+    text-decoration: none;
+    color: #222222 !important; 
+    font-weight: 700;
+}
+.news-card p {
+    color: #444444 !important; 
+    font-size: 0.95em;
+    line-height: 1.5;
+}
+.card-header {
+    display: flex;
+    justify-content: space-between;
+    margin-bottom: 8px;
+}
+.date {
+    font-size: 0.8em;
+    color: #888888 !important;
+}
+
+/* Renk Çizgileri */
 .bullish-card { border-left: 6px solid #4CAF50; }
 .bearish-card { border-left: 6px solid #F44336; }
 .neutral-card { border-left: 6px solid #FFC107; }
-.badge { padding: 3px 8px; border-radius: 4px; font-size: 0.75em; font-weight: bold; color: white; }
+
+/* Badge Tasarımı */
+.badge {
+    padding: 3px 8px;
+    border-radius: 4px;
+    font-size: 0.75em;
+    font-weight: bold;
+    color: white;
+}
 .bullish { background-color: #4CAF50; }
 .bearish { background-color: #F44336; }
 .neutral { background-color: #FFC107; color: #333; }
-.news-feed-container { max-height: 800px; overflow-y: auto; padding-right: 10px; }
+
+.news-feed-container {
+    max-height: 800px;
+    overflow-y: auto;
+    padding-right: 10px;
+}
 """
 
-# --- UI ---
+# --- UI TASARIMI ---
 with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), css=custom_css, title="Crypto News AI") as app:
+    
+    # Üst Bar (Header)
     with gr.Row(elem_id="header"):
         with gr.Column(scale=3):
-            gr.Markdown("# ⚡ AI Crypto Sentiment Dashboard (ZeroGPU)")
+            gr.Markdown("# ⚡ AI Crypto Sentiment Dashboard")
         with gr.Column(scale=1):
             init_btn = gr.Button("🚀 1. Modelleri Başlat", variant="primary", size="sm")
             load_status = gr.Textbox(show_label=False, placeholder="Model Durumu...", lines=1)
+            
     gr.Markdown("---")
+    
+    # Ana İçerik
     with gr.Row():
+        # --- SOL SÜTUN (Kontrol & Analiz) ---
         with gr.Column(scale=1, min_width=300):
+            
             with gr.Group():
                 gr.Markdown("### 📡 Veri Kontrol")
                 refresh_btn = gr.Button("🔄 2. Verileri Yenile", variant="secondary")
                 data_status = gr.Textbox(show_label=False, placeholder="Veri bekleniyor...", lines=1)
                 overview_plot = gr.Plot(label="Piyasa Özeti")
+            
             gr.Markdown("### 🔍 Hızlı Filtre")
             with gr.Tabs():
                 with gr.Tab("Coin Ara"):
                     coin_input = gr.Textbox(placeholder="BTC, ETH, SOL...", show_label=False)
                     coin_btn = gr.Button("Analiz Et")
                     coin_plot = gr.Plot(show_label=False)
+                
                 with gr.Tab("Haber Ara"):
                     search_input = gr.Textbox(placeholder="Konu girin...", show_label=False)
                     search_btn = gr.Button("Ara")
+            
             gr.Markdown("### 🇹🇷 Çeviri Aracı")
             with gr.Group():
                 news_selector = gr.Dropdown(label="Haber Seçin", choices=[], type="value", interactive=True)
                 translate_btn = gr.Button("Türkçeye Çevir")
                 tr_title_out = gr.Textbox(label="Başlık (TR)", lines=2)
                 tr_summary_out = gr.Textbox(label="Özet (TR)", lines=4)
+
+        # --- SAĞ SÜTUN (Haber Akışı) ---
         with gr.Column(scale=2):
             gr.Markdown("### 📰 Canlı Haber Akışı")
             news_feed_html = gr.HTML(label="Haberler", value="<div style='padding:20px; color:#666;'>Veriler yüklenince burada görünecek...</div>")
 
+    # --- ETKİLEŞİMLER (EVENTS) ---
     init_btn.click(initialize_models, outputs=load_status)
-    refresh_btn.click(fetch_news_wrapper, outputs=[data_status, news_feed_html, news_selector, overview_plot])
+    
+    refresh_btn.click(
+        fetch_news_wrapper, 
+        outputs=[data_status, news_feed_html, news_selector, overview_plot]
+    )
+    
     coin_btn.click(analyze_coin, inputs=coin_input, outputs=[coin_plot, news_feed_html])
     search_btn.click(search_news, inputs=search_input, outputs=news_feed_html)
-    translate_btn.click(perform_translation, inputs=news_selector, outputs=[tr_title_out, tr_summary_out])
+    
+    translate_btn.click(
+        perform_translation, 
+        inputs=news_selector, 
+        outputs=[tr_title_out, tr_summary_out]
+    )
 
 if __name__ == "__main__":
     app.launch()