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import re
import string
import gradio as gr
import matplotlib.pyplot as plt
import pandas as pd
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline, pipeline
# FinBERT yükle
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)
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-tr-en", max_length=512)
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 multilingual_batch_classify(user_inputs):
texts = user_inputs.split('\n') # Her satırı ayrı haber
texts = [t for t in texts if t.strip() != ''] # Boş satırları çıkar
results = []
for text in texts:
original_text = text
cleaned_text = clean_text(text)
# 🔍 İngilizce'ye çevir
translation_output = translator(cleaned_text)[0]['translation_text']
translated_text = translation_output
# 🔮 FinBERT ile analiz
outputs = finbert_classifier(translated_text)
outputs = outputs[0]
labels_scores = {out['label']: 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
# 🏷️ Auto-tagging
if best_label == "Positive":
auto_tag = "📈 Opportunity"
elif best_label == "Negative":
auto_tag = "⚠️ Risk Alert"
elif best_label == "Neutral":
auto_tag = "ℹ️ Informational"
else:
auto_tag = "🤔 Unclassified"
results.append((
original_text,
translated_text,
best_label,
best_confidence * 100,
positive_score,
negative_score,
neutral_score,
auto_tag
))
df_results = pd.DataFrame(results, columns=[
"Original Text", "Translated Text", "Predicted Sentiment", "Best Confidence (%)",
"Positive (%)", "Negative (%)", "Neutral (%)", "Auto Tag"
])
return df_results
def multilingual_analyze_and_filter(user_inputs, selected_sentiment):
df_results = multilingual_batch_classify(user_inputs)
if selected_sentiment != "All":
df_results = df_results[df_results["Predicted Sentiment"] == 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"]]
ax.bar(range(len(df_results)), df_results["Best Confidence (%)"], 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 (%)")
ax.set_title(f"Sentiment Confidence Trend ({selected_sentiment})")
plt.tight_layout()
return df_results, fig
with gr.Blocks() as demo:
gr.Markdown("# 🧠🌍 FinBERT Multilingual Financial News Analyzer")
gr.Markdown("Paste multiple news headlines (any language!) and see full sentiment analysis after translation!")
user_input = gr.Textbox(lines=10, placeholder="Enter each news headline on a new line...", label="📝 News Headlines (any language)")
sentiment_filter = gr.Dropdown(["All", "Positive", "Negative", "Neutral"], label="🎯 Filter by Sentiment", value="All")
output_table = gr.Dataframe(label="🔮 Prediction Table (with Auto Tags and Translation)")
output_plot = gr.Plot(label="📈 Sentiment Confidence Trend")
submit_button = gr.Button("Analyze News Batch (Multilingual)")
submit_button.click(fn=multilingual_analyze_and_filter, inputs=[user_input, sentiment_filter], outputs=[output_table, output_plot])
# App başlat
demo.launch()
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