Update app.py

app.py

@@ -1,392 +1,161 @@
 import streamlit as st
-from transformers import pipeline
-from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
 import pandas as pd
-from datetime import datetime, timedelta
-import plotly.graph_objects as go
-from wordcloud import WordCloud
-import base64
-from io import BytesIO
+import requests
+import numpy as np
+import plotly.express as px
 import nltk
-from textblob import TextBlob
-import os
 import time
-from functools import lru_cache
-import numpy as np
-from sklearn.linear_model import Ridge
-from sklearn.preprocessing import PolynomialFeatures
-from sklearn.pipeline import make_pipeline
-import feedparser
-
-# --------------------------
-# Initial Setup
-# --------------------------
+from datetime import datetime, timedelta
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+from textblob import TextBlob
+from transformers import pipeline
+from wordcloud import WordCloud
+from sklearn.linear_model import LinearRegression
+import matplotlib.pyplot as plt
 
-st.set_page_config(
-    page_title="🚀 SentimentSync Pro",
-    page_icon="📈",
-    layout="wide"
-)
+# =========================================
+# SETUP
+# =========================================
+st.set_page_config(page_title="Financial Sentiment Analyzer", layout="wide")
 
-# --------------------------
-# Performance Optimizations
-# --------------------------
+nltk.download("stopwords", quiet=True)
 
+# โหลดโมเดล sentiment ของ BERT
 @st.cache_resource
-def load_models():
-    """Load models with progress indicators"""
-    progress = st.progress(0, text="Loading sentiment models...")
-
-    try:
-        with st.spinner("Loading BERT model..."):
-            bert_sentiment = pipeline(
-                "sentiment-analysis",
-                model="nlptown/bert-base-multilingual-uncased-sentiment"
-            )
-        progress.progress(50)
-
-        with st.spinner("Loading VADER analyzer..."):
-            vader_analyzer = SentimentIntensityAnalyzer()
-        progress.progress(100)
+def load_bert_model():
+    return pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
 
-        return bert_sentiment, vader_analyzer
-    except Exception as e:
-        st.error(f"Model loading failed: {str(e)}")
-        return None, None
+bert_model = load_bert_model()
+vader = SentimentIntensityAnalyzer()
 
-# --------------------------
-# Fetch Financial News
-# --------------------------
+# ใส่ API key ของคุณ
+API_KEY = st.secrets["NEWS_API_KEY"]  # ใส่ใน .streamlit/secrets.toml
+# หรือถ้ารัน local:
+# API_KEY = "88bc396d4eab4be494a4b86ec842db47"
 
+# =========================================
+# FUNCTION: ดึงข่าวจาก NewsAPI.org
+# =========================================
 @st.cache_data(ttl=3600, show_spinner="Fetching financial news...")
-def fetch_financial_news(keyword, limit=30):
-    """Fetch recent financial news (past 7 days) using Google News RSS"""
-    try:
-        base_url = "https://news.google.com/rss/search"
-        query = f"{keyword}+finance+stock"
-        feed_url = f"{base_url}?q={query}&hl=en-US&gl=US&ceid=US:en"
-
-        feed = feedparser.parse(feed_url)
-        seven_days_ago = datetime.now() - timedelta(days=7)
-
-        articles = []
-        for entry in feed.entries:
-            published = None
-            if hasattr(entry, 'published_parsed') and entry.published_parsed:
-                published = datetime(*entry.published_parsed[:6])
-            elif hasattr(entry, 'updated_parsed') and entry.updated_parsed:
-                published = datetime(*entry.updated_parsed[:6])
-            else:
-                continue
-
-            if published < seven_days_ago:
-                continue
-
-            text = f"{entry.title}\n{entry.summary}" if hasattr(entry, 'summary') else entry.title
-
-            articles.append({
-                'date': published,
-                'text': text,
-                'source': 'Financial News',
-                'url': entry.link
-            })
-
-            if len(articles) >= limit:
-                break
-
-        return pd.DataFrame(articles)
-
-    except Exception as e:
-        st.error(f"News fetch error: {str(e)}")
+def fetch_financial_news(keyword, days=7, limit=50):
+    """ดึงข่าวการเงินย้อนหลังจาก NewsAPI.org"""
+    to_date = datetime.now().strftime('%Y-%m-%d')
+    from_date = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
+
+    url = (
+        f"https://newsapi.org/v2/everything?"
+        f"q={keyword}+finance+stock&"
+        f"from={from_date}&to={to_date}&"
+        f"language=en&"
+        f"sortBy=publishedAt&"
+        f"pageSize={limit}&"
+        f"apiKey={API_KEY}"
+    )
+
+    response = requests.get(url)
+    data = response.json()
+
+    if data.get("status") != "ok":
+        st.error(f"Error fetching news: {data.get('message', 'Unknown error')}")
         return pd.DataFrame()
 
-# --------------------------
-# Sentiment Analysis
-# --------------------------
-
-def analyze_text(text, models):
-    bert_sentiment, vader_analyzer = models
-    truncated_text = text[:2000] if text else ""
-
-    try:
-        if not truncated_text.strip():
-            return {
-                'vader': 0,
-                'bert': 0,
-                'textblob': 0,
-                'bert_label': 'Neutral',
-                'bert_confidence': 0
-            }
-
-        vader_score = vader_analyzer.polarity_scores(truncated_text)['compound']
-        textblob_score = TextBlob(truncated_text).sentiment.polarity
-
-        bert_result = bert_sentiment(truncated_text[:512])[0]
-
-        label_map = {
-            '1 star': -1,
-            '2 stars': -0.5,
-            '3 stars': 0,
-            '4 stars': 0.5,
-            '5 stars': 1
-        }
-        bert_num = label_map.get(bert_result['label'], 0)
-
-        return {
-            'vader': vader_score,
-            'bert': bert_num,
-            'textblob': textblob_score,
-            'bert_label': bert_result['label'],
-            'bert_confidence': bert_result['score']
-        }
-    except Exception as e:
-        st.error(f"Analysis error: {str(e)}")
-        return {
-            'vader': 0,
-            'bert': 0,
-            'textblob': 0,
-            'bert_label': 'Error',
-            'bert_confidence': 0
-        }
-
-# --------------------------
-# Visualization
-# --------------------------
-
-def generate_wordcloud(text):
-    try:
-        if not text.strip():
-            return ""
-
-        wordcloud = WordCloud(
-            width=800,
-            height=400,
-            background_color='white',
-            collocations=False,
-            stopwords=nltk.corpus.stopwords.words('english')
-        ).generate(text)
-
-        img = BytesIO()
-        wordcloud.to_image().save(img, format='PNG')
-        return base64.b64encode(img.getvalue()).decode()
-    except Exception as e:
-        st.error(f"Word cloud generation error: {str(e)}")
-        return ""
-
-# --------------------------
-# Prediction & Plotting
-# --------------------------
-
-def prepare_data_for_prediction(data):
-    try:
-        if data.empty:
-            st.warning("No data available for prediction")
-            return None
-
-        data = data.sort_values('date')
-        data = data.dropna(subset=['average'])
-        daily_data = data.groupby(pd.Grouper(key='date', freq='D'))['average'].mean().reset_index()
-        daily_data = daily_data.dropna(subset=['average'])
-
-        if len(daily_data) < 5:
-            st.warning("Insufficient valid data points for prediction (minimum 5 required)")
-            return None
-
-        daily_data['days'] = (daily_data['date'] - daily_data['date'].min()).dt.days
-        return daily_data
-    except Exception as e:
-        st.error(f"Data preparation error: {str(e)}")
-        return None
-
-def train_sentiment_model(data):
-    try:
-        if data is None or len(data) < 5:
-            return None, None
-
-        X = data['days'].values.reshape(-1, 1)
-        y = data['average'].values
-
-        model = make_pipeline(PolynomialFeatures(degree=2), Ridge(alpha=1.0))
-        model.fit(X, y)
-
-        return model, data
-    except Exception as e:
-        st.error(f"Model training error: {str(e)}")
-        return None, None
-
-def predict_future_sentiment(model, training_data, days_to_predict=15):
-    try:
-        if model is None or training_data is None:
-            return None
-
-        last_date = training_data['date'].max()
-        future_dates = [last_date + timedelta(days=i) for i in range(1, days_to_predict + 1)]
-        min_date = training_data['date'].min()
-        future_days = [(date - min_date).days for date in future_dates]
-        X_future = np.array(future_days).reshape(-1, 1)
-
-        predictions = model.predict(X_future)
-
-        pred_df = pd.DataFrame({
-            'date': future_dates,
-            'average': predictions,
-            'type': 'prediction'
+    articles = []
+    for a in data["articles"]:
+        articles.append({
+            "date": pd.to_datetime(a["publishedAt"]),
+            "text": f"{a['title']}\n{a.get('description', '')}",
+            "source": a["source"]["name"],
+            "url": a["url"]
         })
 
-        training_df = training_data.copy()
-        training_df['type'] = 'actual'
+    return pd.DataFrame(articles)
 
-        return pd.concat([training_df, pred_df], ignore_index=True)
-    except Exception as e:
-        st.error(f"Prediction error: {str(e)}")
-        return None
-
-def plot_sentiment(data, keyword):
+# =========================================
+# FUNCTION: วิเคราะห์อารมณ์
+# =========================================
+def analyze_sentiment(text):
+    """รวมผลจาก BERT, VADER, TextBlob"""
     try:
-        if data is None or data.empty:
-            st.warning("No data available for plotting sentiment trends")
-            return None
-
-        actual_data = data[data['type'] == 'actual']
-        pred_data = data[data['type'] == 'prediction']
-
-        fig = go.Figure()
-
-        if not actual_data.empty:
-            fig.add_trace(go.Scatter(
-                x=actual_data['date'],
-                y=actual_data['average'],
-                name='Actual Sentiment',
-                mode='lines+markers',
-                line=dict(color='#636EFA')
-            ))
-
-        if not pred_data.empty:
-            fig.add_trace(go.Scatter(
-                x=pred_data['date'],
-                y=pred_data['average'],
-                name='Predicted Sentiment',
-                mode='lines+markers',
-                line=dict(color='#EF553B', dash='dot')
-            ))
-
-        fig.update_layout(
-            title=f'Sentiment Analysis and Prediction for "{keyword}"',
-            xaxis_title="Date",
-            yaxis_title="Sentiment Score",
-            hovermode="x unified",
-            legend_title="Data Type"
+        bert_label = bert_model(text[:512])[0]["label"]
+        vader_score = vader.polarity_scores(text)["compound"]
+        blob_score = TextBlob(text).sentiment.polarity
+
+        bert_score = (
+            1 if "5" in bert_label or "4" in bert_label
+            else -1 if "1" in bert_label or "2" in bert_label
+            else 0
         )
 
-        return fig
-    except Exception as e:
-        st.error(f"Plotting error: {str(e)}")
-        return None
-
-# --------------------------
-# Main App
-# --------------------------
-
-def main():
-    st.title("🚀 SentimentSync Pro - Financial News Sentiment Dashboard")
-
-    with st.sidebar:
-        st.header("🔧 Analysis Controls")
-        analysis_mode = st.radio(
-            "Mode",
-            ["Text Analysis", "Financial News Analysis"],
-            index=1
-        )
-
-        if analysis_mode == "Text Analysis":
-            user_input = st.text_area("Enter text to analyze", height=200, placeholder="Paste your content here...")
-            analyze_btn = st.button("Analyze Now")
-        else:
-            keyword = st.text_input("Enter keyword (e.g., Apple, Tesla, Bitcoin)")
-            analyze_btn = st.button("Fetch & Analyze")
-
-        st.markdown("---")
-        show_details = st.checkbox("Show detailed results", value=False)
-        enable_prediction = st.checkbox("Enable sentiment prediction", value=True)
-        st.markdown("---")
-
-    if analyze_btn:
-        models = load_models()
-        if not all(models):
-            st.error("Model loading failed")
-            return
-
-        if analysis_mode == "Text Analysis":
-            if not user_input.strip():
-                st.warning("Please enter some text")
-                return
-
-            with st.spinner("Analyzing..."):
-                result = analyze_text(user_input, models)
-                st.success("✅ Analysis completed")
-
-                cols = st.columns(3)
-                cols[0].metric("VADER Score", f"{result['vader']:.2f}")
-                cols[1].metric("BERT Label", result['bert_label'])
-                cols[2].metric("TextBlob", f"{result['textblob']:.2f}")
-
-                st.subheader("📊 Word Cloud")
-                wc_img = f"data:image/png;base64,{generate_wordcloud(user_input)}"
-                st.image(wc_img, use_column_width=True)
-
-        else:
-            if not keyword.strip():
-                st.warning("Please enter a keyword")
-                return
-
-            with st.spinner(f"Fetching financial news for '{keyword}'..."):
-                start_time = time.time()
-                news_data = fetch_financial_news(keyword)
-                if news_data.empty:
-                    st.error("No news found for the past 7 days.")
-                    return
-
-                analysis_results = []
-                for _, row in news_data.iterrows():
-                    analysis_results.append(analyze_text(row['text'], models))
-
-                news_data['vader'] = [r['vader'] for r in analysis_results]
-                news_data['bert'] = [r['bert'] for r in analysis_results]
-                news_data['textblob'] = [r['textblob'] for r in analysis_results]
-                news_data['average'] = news_data[['vader', 'bert', 'textblob']].mean(axis=1)
-
-                processing_time = time.time() - start_time
-                st.success(f"Analyzed {len(news_data)} articles in {processing_time:.2f}s")
-
-                avg_sentiment = news_data['average'].mean()
-                cols = st.columns(3)
-                cols[0].metric("Avg Sentiment", f"{avg_sentiment:.2f}")
-                cols[1].metric("Positive", f"{(news_data['average'] > 0.1).mean() * 100:.1f}%")
-                cols[2].metric("Negative", f"{(news_data['average'] < -0.1).mean() * 100:.1f}%")
-
-                all_text = " ".join(news_data['text'])
-                wc_img = f"data:image/png;base64,{generate_wordcloud(all_text)}"
-                st.subheader("📊 Word Cloud")
-                st.image(wc_img, use_column_width=True)
-
-                if enable_prediction:
-                    daily_data = prepare_data_for_prediction(news_data)
-                    model, training_data = train_sentiment_model(daily_data)
-                    if model is not None:
-                        full_data = predict_future_sentiment(model, training_data)
-                        fig = plot_sentiment(full_data, keyword)
-                        st.plotly_chart(fig, use_container_width=True)
-
-                if show_details:
-                    st.subheader("📰 Detailed News Data")
-                    st.dataframe(news_data[['date', 'source', 'text', 'average', 'url']], use_container_width=True)
-
-
-if __name__ == "__main__":
-    try:
-        nltk.data.path.append(os.path.join(os.path.expanduser("~"), "nltk_data"))
-        nltk.download('stopwords', quiet=True)
-    except:
-        pass
-
-    main()
+        final_score = np.mean([bert_score, np.sign(vader_score), np.sign(blob_score)])
+        return final_score
+    except Exception:
+        return 0
+
+# =========================================
+# FUNCTION: สร้าง Word Cloud
+# =========================================
+def create_wordcloud(texts):
+    text = " ".join(texts)
+    wc = WordCloud(width=800, height=400, background_color="white",
+                   stopwords=set(nltk.corpus.stopwords.words("english"))).generate(text)
+    return wc
+
+# =========================================
+# FUNCTION: พยากรณ์แนวโน้มอารมณ์
+# =========================================
+def forecast_sentiment_trend(df):
+    df = df.sort_values("date")
+    df["timestamp"] = (df["date"] - df["date"].min()).dt.days
+    model = LinearRegression()
+    model.fit(df[["timestamp"]], df["sentiment"])
+    future = pd.DataFrame({"timestamp": np.arange(df["timestamp"].max()+1, df["timestamp"].max()+8)})
+    pred = model.predict(future)
+    return pred
+
+# =========================================
+# MAIN APP
+# =========================================
+st.title("💹 Financial News Sentiment Analyzer (NewsAPI.org version)")
+st.markdown("วิเคราะห์อารมณ์ของข่าวการเงินย้อนหลังจาก **NewsAPI.org** โดยใช้ BERT + VADER + TextBlob")
+
+keyword = st.text_input("🔍 ใส่ชื่อบริษัท / หุ้น / คำค้นหา", "Tesla")
+limit = st.slider("จำนวนข่าวที่ต้องการดึง", 10, 100, 50)
+
+if st.button("เริ่มวิเคราะห์ข่าว"):
+    with st.spinner(f"กำลังดึงข่าวเกี่ยวกับ '{keyword}' ..."):
+        news_df = fetch_financial_news(keyword, days=7, limit=limit)
+
+    if news_df.empty:
+        st.error("❌ ไม่พบข่าวในช่วง 7 วันที่ผ่านมา")
+        st.stop()
+
+    st.success(f"✅ ดึงข่าวได้ {len(news_df)} รายการจาก NewsAPI.org")
+
+    # วิเคราะห์ sentiment
+    st.info("🔎 กำลังวิเคราะห์อารมณ์ของข่าวแต่ละรายการ...")
+    news_df["sentiment"] = news_df["text"].apply(analyze_sentiment)
+
+    # แสดงผลรวม
+    avg_sentiment = news_df["sentiment"].mean()
+    st.metric("📊 ค่าเฉลี่ยอารมณ์โดยรวม", f"{avg_sentiment:.2f}")
+
+    # กราฟแนวโน้ม
+    fig = px.line(news_df.sort_values("date"), x="date", y="sentiment",
+                  title=f"แนวโน้มอารมณ์ของข่าว '{keyword}'",
+                  markers=True)
+    st.plotly_chart(fig, use_container_width=True)
+
+    # Word Cloud
+    st.subheader("☁️ คำที่ถูกใช้บ่อยในข่าว")
+    wc = create_wordcloud(news_df["text"].tolist())
+    st.image(wc.to_array())
+
+    # พยากรณ์แนวโน้ม
+    st.subheader("📈 พยากรณ์แนวโน้มอารมณ์ใน 7 วันข้างหน้า")
+    forecast = forecast_sentiment_trend(news_df)
+    st.line_chart(forecast)
+
+    # แสดงข่าวต้นฉบับ
+    st.subheader("📰 ข่าวที่ใช้ในการวิเคราะห์")
+    for _, row in news_df.iterrows():
+        st.markdown(f"**[{row['source']}]({row['url']})** — {row['date'].strftime('%Y-%m-%d')}  \n{row['text']}")