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app.py

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+# 📚 Gerekli Kütüphaneler (Libraries)
+import re
+import string
+import gradio as gr
+import matplotlib.pyplot as plt
+import pandas as pd
+from langdetect import detect  # Dil tespiti için
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
+
+finbert_model_name = "yiyanghkust/finbert-tone"
+finbert_tokenizer = AutoTokenizer.from_pretrained(finbert_model_name)
+finbert_model = AutoModelForSequenceClassification.from_pretrained(finbert_model_name)
+finbert_classifier = TextClassificationPipeline(model=finbert_model, tokenizer=finbert_tokenizer, top_k=None)
+
+xlm_model_name = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
+xlm_tokenizer = AutoTokenizer.from_pretrained(xlm_model_name)
+xlm_model = AutoModelForSequenceClassification.from_pretrained(xlm_model_name)
+xlm_classifier = TextClassificationPipeline(model=xlm_model, tokenizer=xlm_tokenizer, top_k=None)
+
+
+def clean_text(text):
+    text = text.lower()
+    text = re.sub(r'\n', ' ', text)
+    text = re.sub(r'\s+', ' ', text).strip()
+    text = re.sub(r'[%s]' % re.escape(string.punctuation), '', text)
+    return text
+
+def smart_batch_classify(user_inputs):
+    texts = user_inputs.split('\n')  # Her satır bir haber
+    texts = [t for t in texts if t.strip() != '']  # Boşları çıkar
+
+    results = []
+    for text in texts:
+        original_text = text
+        cleaned_text = clean_text(text)
+
+        
+        try:
+            lang = detect(cleaned_text)
+        except:
+            lang = "unknown"
+
+       
+        if lang == "en":
+            outputs = finbert_classifier(cleaned_text)[0]
+        else:
+            outputs = xlm_classifier(cleaned_text)[0]
+
+        labels_scores = {out['label'].lower(): out['score'] for out in outputs}
+        best_label = max(labels_scores, key=labels_scores.get)
+        best_confidence = labels_scores[best_label]
+
+        positive_score = labels_scores.get("positive", 0) * 100
+        negative_score = labels_scores.get("negative", 0) * 100
+        neutral_score = labels_scores.get("neutral", 0) * 100
+
+       
+        if best_label == "positive":
+            auto_tag = "📈 Opportunity (Fırsat)"
+        elif best_label == "negative":
+            auto_tag = "⚠️ Risk Alert (Risk Uyarısı)"
+        elif best_label == "neutral":
+            auto_tag = "ℹ️ Informational (Bilgilendirme)"
+        else:
+            auto_tag = "🤔 Unclassified (Sınıflandırılamadı)"
+
+        results.append((
+            original_text,
+            lang,
+            best_label,
+            best_confidence * 100,
+            positive_score,
+            negative_score,
+            neutral_score,
+            auto_tag
+        ))
+
+    df_results = pd.DataFrame(results, columns=[
+        "Original Text (Orijinal Metin)",
+        "Detected Language (Tespit Edilen Dil)",
+        "Predicted Sentiment (Tahmin Edilen Duygu)",
+        "Best Confidence (%) (En Yüksek Güven %)",
+        "Positive (%) (Pozitif %)",
+        "Negative (%) (Negatif %)",
+        "Neutral (%) (Nötr %)",
+        "Auto Tag (Otomatik Etiket)"
+    ])
+
+    return df_results
+
+
+def smart_analyze_and_filter(user_inputs, selected_sentiment):
+    df_results = smart_batch_classify(user_inputs)
+
+    if selected_sentiment != "All":
+        df_results = df_results[df_results["Predicted Sentiment (Tahmin Edilen Duygu)"] == selected_sentiment]
+
+    fig, ax = plt.subplots(figsize=(10,6))
+    sentiment_colors = {
+        "positive": "green",
+        "negative": "red",
+        "neutral": "blue"
+    }
+
+    color_list = [sentiment_colors.get(sent, "gray") for sent in df_results["Predicted Sentiment (Tahmin Edilen Duygu)"].str.lower()]
+
+    ax.bar(range(len(df_results)), df_results["Best Confidence (%) (En Yüksek Güven %)"], color=color_list)
+    ax.set_xticks(range(len(df_results)))
+    ax.set_xticklabels([f"News {i+1}" for i in range(len(df_results))], rotation=45)
+    ax.set_ylabel("Best Confidence (%) (En Yüksek Güven %)")
+    ax.set_title(f"Sentiment Confidence Trend ({selected_sentiment}) (Duygu Güveni Trend)")
+
+    plt.tight_layout()
+
+    return df_results, fig
+
+
+with gr.Blocks() as demo:
+    # 🟩 Başlığı yeşil yaptık
+    gr.Markdown("<h1 style='color: green;'>🧠🌍📰 Smart Multilingual Financial News Analyzer</h1>")
+    gr.Markdown("Paste multiple news headlines (any language!) and get accurate sentiment prediction!")
+    gr.Markdown("## 🧠🌍📰 Çok Dilli Finansal Haber Analizörü")
+    gr.Markdown("Birden fazla haber başlığını yapıştırın ve doğru modelle otomatik analiz edin!")
+    gr.Markdown("Haberleri satır satır analiz ediyorum.🗞️📰")
+
+    
+    gr.HTML("""
+    <div style='text-align: center; color: red; font-size: 12px; font-weight: bold;'>
+        🎂 Doğum Günün Kutlu Olsun Babacım, Ali Sakin! 🎉❤️
+    </div>
+    """)
+    gr.HTML("""
+    <div style='text-align: center; color: gray; font-size: 4px;'>
+        (Happy Birthday My Father, Ali Sakin ��)
+    </div>
+    """)
+
+    user_input = gr.Textbox(lines=10, placeholder="Enter each news headline (any language)...", label="📝 News Headlines (Haber Başlıkları)")
+    sentiment_filter = gr.Dropdown(["All", "positive", "negative", "neutral"], label="🎯 Filter by Sentiment (Duyguya Göre Filtrele)", value="All")
+
+    output_table = gr.Dataframe(label="🔮 Prediction Table (Otomatik Etiketli Tahmin Tablosu)")
+    output_plot = gr.Plot(label="📈 Sentiment Confidence Trend (Duygu Güveni Trend Grafiği)")
+
+    submit_button = gr.Button("Analyze News Batch (Haberleri Analiz Et)")
+
+    submit_button.click(fn=smart_analyze_and_filter, inputs=[user_input, sentiment_filter], outputs=[output_table, output_plot])
+
+
+demo.launch()

requierements.txt

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+gradio
+matplotlib
+pandas
+transformers
+langdetect
+torch
+sentencepiece
+protobuf