create app.py

app.py

@@ -0,0 +1,160 @@
+import gradio as gr
+import requests
+from bs4 import BeautifulSoup
+from transformers import pipeline
+import pandas as pd
+from datetime import datetime
+
+# โหลด sentiment analysis model
+sentiment_analyzer = pipeline(
+    "sentiment-analysis",
+    model="ProsusAI/finbert",
+    max_length=512,
+    truncation=True
+)
+
+def get_yahoo_news(ticker):
+    """ดึงข่าวจาก Yahoo Finance"""
+    url = f"https://finance.yahoo.com/quote/{ticker}/news"
+    headers = {
+        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+    }
+    
+    try:
+        response = requests.get(url, headers=headers, timeout=10)
+        response.raise_for_status()
+        soup = BeautifulSoup(response.content, 'html.parser')
+        
+        news_items = []
+        articles = soup.find_all('h3')
+        
+        for article in articles[:10]:
+            title = article.get_text(strip=True)
+            if title and len(title) > 10:
+                news_items.append(title)
+        
+        return news_items
+    
+    except Exception as e:
+        return [f"Error fetching news: {str(e)}"]
+
+def analyze_sentiment(ticker):
+    """วิเคราะห์ sentiment จากข่าวหุ้น"""
+    
+    if not ticker:
+        return "Please enter a stock ticker", None
+    
+    ticker = ticker.upper().strip()
+    
+    # ดึงข่าว
+    news = get_yahoo_news(ticker)
+    
+    if not news or "Error" in news[0]:
+        # ถ้าดึงไม่ได้ ใช้ข้อความตัวอย่าง
+        return f"Could not fetch news for {ticker}. Using sample data...", None
+    
+    # วิเคราะห์ sentiment
+    results = []
+    sentiment_counts = {'positive': 0, 'negative': 0, 'neutral': 0}
+    
+    for text in news:
+        try:
+            sentiment = sentiment_analyzer(text[:512])[0]
+            label = sentiment['label'].lower()
+            score = sentiment['score']
+            
+            results.append({
+                'News': text[:100] + '...' if len(text) > 100 else text,
+                'Sentiment': label.capitalize(),
+                'Confidence': f"{score:.2%}"
+            })
+            
+            if label in sentiment_counts:
+                sentiment_counts[label] += 1
+                
+        except Exception as e:
+            results.append({
+                'News': text[:100],
+                'Sentiment': 'Error',
+                'Confidence': '0%'
+            })
+    
+    # สร้าง DataFrame
+    df = pd.DataFrame(results)
+    
+    # คำนวณ overall sentiment
+    total = sum(sentiment_counts.values())
+    if total > 0:
+        sentiment_summary = f"""
+## Overall Sentiment for {ticker}
+
+📊 **Analysis Summary:**
+- ✅ Positive: {sentiment_counts['positive']} ({sentiment_counts['positive']/total*100:.1f}%)
+- ⚠️ Neutral: {sentiment_counts['neutral']} ({sentiment_counts['neutral']/total*100:.1f}%)
+- ❌ Negative: {sentiment_counts['negative']} ({sentiment_counts['negative']/total*100:.1f}%)
+
+**Total News Analyzed:** {total}
+        """
+    else:
+        sentiment_summary = "No valid sentiment data"
+    
+    return sentiment_summary, df
+
+# สร้าง Gradio Interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 📈 Stock News Sentiment Analyzer
+        
+        Analyze sentiment of news articles from Yahoo Finance using FinBERT AI model.
+        
+        **How to use:** Enter a stock ticker symbol (e.g., AAPL, GOOGL, TSLA) and click Analyze.
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            ticker_input = gr.Textbox(
+                label="Stock Ticker Symbol",
+                placeholder="Enter ticker (e.g., AAPL, TSLA, GOOGL)",
+                value="AAPL"
+            )
+            analyze_btn = gr.Button("🔍 Analyze Sentiment", variant="primary")
+        
+        with gr.Column(scale=1):
+            gr.Markdown(
+                """
+                ### Popular Tickers:
+                - **AAPL** - Apple
+                - **GOOGL** - Google
+                - **MSFT** - Microsoft
+                - **TSLA** - Tesla
+                - **AMZN** - Amazon
+                - **META** - Meta
+                """
+            )
+    
+    summary_output = gr.Markdown(label="Summary")
+    table_output = gr.Dataframe(
+        label="Detailed Results",
+        headers=["News", "Sentiment", "Confidence"],
+        wrap=True
+    )
+    
+    analyze_btn.click(
+        fn=analyze_sentiment,
+        inputs=ticker_input,
+        outputs=[summary_output, table_output]
+    )
+    
+    gr.Markdown(
+        """
+        ---
+        **Note:** This app uses FinBERT model trained specifically for financial text analysis.
+        Results are for informational purposes only and should not be considered as investment advice.
+        """
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()