Update app.py

app.py

@@ -55,9 +55,10 @@ def analyze_text(text):
     with torch.no_grad():
         outputs = model(**inputs)
         logits = outputs.logits
-        probs = torch.softmax(logits, dim=1).numpy()[0]  # FinBERT = [negative, neutral, positive]
+        probs = torch.softmax(logits, dim=1).numpy()[0]
+        # FinBERT = [negative, neutral, positive]
         score = (-1 * probs[0]) + (0 * probs[1]) + (1 * probs[2])
-        return float(score)
+    return float(score)
 
 def summarize_themes(news_texts):
     """สรุปธีมข่าวด้วย Zero-shot classification"""
@@ -166,8 +167,10 @@ def main():
     st.markdown("วิเคราะห์แนวโน้มอารมณ์ของข่าวย้อนหลัง 7 วัน พร้อมราคาหุ้น")
 
     # Sidebar
-    keyword = st.text_input("ค้นหา Stock Symbol (เช่น AAPL, TSLA):", "")
-    analyze_btn = st.button("วิเคราะห์เลย")
+    with st.sidebar:
+        keyword = st.text_input("ค้นหา Stock Symbol (เช่น AAPL, TSLA):", "")
+        analyze_btn = st.button("วิเคราะห์เลย")
+
     if not analyze_btn:
         st.info("กรอกคำค้นแล้วกด 'วิเคราะห์เลย'")
         return
@@ -175,6 +178,7 @@ def main():
     # ดึงข่าว
     st.info(f"กำลังดึงข่าวย้อนหลัง 7 วันสำหรับ '{keyword}'...")
     news_df = fetch_financial_news(keyword)
+
     if news_df.empty:
         st.warning("ไม่พบบทความข่าว")
         return
@@ -188,18 +192,23 @@ def main():
     avg_sentiment = news_df["sentiment"].mean()
     pos_pct = (news_df["sentiment"] > 0.1).mean() * 100
     neg_pct = (news_df["sentiment"] < -0.1).mean() * 100
+
     col1, col2, col3 = st.columns(3)
     col1.metric("ค่าเฉลี่ยอารมณ์ข่าว", f"{avg_sentiment:.2f}")
     col2.metric("ข่าวเชิงบวก", f"{pos_pct:.1f}%")
     col3.metric("ข่าวเชิงลบ", f"{neg_pct:.1f}%")
 
+    # ---------------------------------------------------------
     # ธีมข่าวแทน Word Cloud
+    # ---------------------------------------------------------
     st.subheader("📰 ธีมข่าว (Top Theme per Article)")
     news_df["theme"] = summarize_themes(news_df["text"].tolist())
     theme_counts = news_df["theme"].value_counts()
     st.bar_chart(theme_counts)
 
-    # ส่วนกราฟ Sentiment & Price
+    # ---------------------------------------------------------
+    # ส่วนกราฟ Sentiment & Price (เหมือนเดิม)
+    # ---------------------------------------------------------
     st.subheader("📈 แนวโน้มอารมณ์ของข่าว & ราคาหุ้น")
     news_df["date_day"] = pd.to_datetime(news_df["date"].dt.date)
 
@@ -211,6 +220,7 @@ def main():
         return "neutral"
 
     news_df["sentiment_type"] = news_df["sentiment"].apply(sentiment_type)
+
     daily_avg = news_df.groupby("date_day")["sentiment"].mean().reset_index(name="avg_sentiment")
     daily_counts = news_df.groupby(["date_day", "sentiment_type"]).size().unstack(fill_value=0).reset_index()
     df_sorted = pd.merge(daily_avg, daily_counts, on="date_day").sort_values("date_day")
@@ -225,6 +235,7 @@ def main():
     min_date, max_date = df_sorted["date_day"].min(), df_sorted["date_day"].max()
     st.info(f"กำลังดึงราคาหุ้น {symbol} ...")
     stock_df = fetch_stock_price(symbol, min_date, max_date)
+
     plot_data = pd.merge(df_sorted, stock_df, left_on="date_day", right_on="date", how="left")
 
     # Correlation
@@ -239,9 +250,11 @@ def main():
     # Forecast Sentiment
     plot_data["timestamp"] = (plot_data["date_day"] - plot_data["date_day"].min()).dt.days
     train_data = plot_data.dropna(subset=['avg_sentiment'])
+
     if len(train_data) >= 2:
         model_lr = LinearRegression()
         model_lr.fit(train_data[["timestamp"]], train_data["avg_sentiment"])
+
         future_days = 7
         future_timestamps = np.arange(
             plot_data["timestamp"].max() + 1,
@@ -250,71 +263,44 @@ def main():
         future_dates = [plot_data["date_day"].max() + timedelta(days=i) for i in range(1, future_days + 1)]
         future_preds = model_lr.predict(future_timestamps.reshape(-1, 1))
 
-    # Plot
-    fig = make_subplots(
-        rows=2, cols=1,
-        specs=[[{"secondary_y": True}], [{}]],
-        row_heights=[0.7, 0.3],
-        vertical_spacing=0.1,
-        shared_xaxes=True
-    )
+        # Plot
+        fig = make_subplots(rows=2, cols=1, specs=[[{"secondary_y": True}], [{}]], row_heights=[0.7, 0.3], vertical_spacing=0.1, shared_xaxes=True)
 
-    # ราคาหุ้น
-    fig.add_trace(
-        go.Scatter(x=plot_data["date_day"], y=plot_data["price"], name=f"{symbol} Price",
-                   mode="lines+markers", line=dict(color="orange")),
-        row=1, col=1, secondary_y=False
-    )
+        # ราคาหุ้น
+        fig.add_trace(go.Scatter(x=plot_data["date_day"], y=plot_data["price"], name=f"{symbol} Price", mode="lines+markers", line=dict(color="orange")), row=1, col=1, secondary_y=False)
 
-    # Sentiment จริง
-    fig.add_trace(
-        go.Scatter(x=plot_data["date_day"], y=plot_data["avg_sentiment"], name="Actual Sentiment",
-                   mode="lines+markers", line=dict(color="blue")),
-        row=1, col=1, secondary_y=True
-    )
+        # Sentiment จริง
+        fig.add_trace(go.Scatter(x=plot_data["date_day"], y=plot_data["avg_sentiment"], name="Actual Sentiment", mode="lines+markers", line=dict(color="blue")), row=1, col=1, secondary_y=True)
 
-    # Sentiment พยากรณ์
-    if "future_preds" in locals():
-        fig.add_trace(
-            go.Scatter(x=future_dates, y=future_preds, name="Predicted Sentiment",
-                       mode="lines+markers", line=dict(color="#05a0fa", dash="dash")),
-            row=1, col=1, secondary_y=True
-        )
+        # Sentiment พยากรณ์
+        if "future_preds" in locals():
+            fig.add_trace(go.Scatter(x=future_dates, y=future_preds, name="Predicted Sentiment", mode="lines+markers", line=dict(color="#05a0fa", dash="dash")), row=1, col=1, secondary_y=True)
 
         # เส้นเชื่อม Actual -> Predicted
         last_actual_date = plot_data["date_day"].max()
         last_actual_value = plot_data["avg_sentiment"].iloc[-1]
         first_pred_date = future_dates[0]
         first_pred_value = future_preds[0]
-        fig.add_trace(
-            go.Scatter(x=[last_actual_date, first_pred_date], y=[last_actual_value, first_pred_value],
-                       mode="lines", line=dict(color="#05a0fa", dash="dot"),
-                       name="Connector Actual→Predicted"),
-            row=1, col=1, secondary_y=True
-        )
+        fig.add_trace(go.Scatter(x=[last_actual_date, first_pred_date], y=[last_actual_value, first_pred_value], mode="lines", line=dict(color="#05a0fa", dash="dot"), name="Connector Actual→Predicted"), row=1, col=1, secondary_y=True)
 
-    # จำนวนข่าว
-    for col in ["neutral", "negative", "positive"]:
-        if col not in plot_data.columns:
-            plot_data[col] = 0
-    fig.add_trace(go.Bar(x=plot_data["date_day"], y=plot_data["neutral"], name="Neutral",
-                         marker_color='rgba(128, 128, 128, 0.7)'), row=2, col=1)
-    fig.add_trace(go.Bar(x=plot_data["date_day"], y=plot_data["negative"], name="Negative",
-                         marker_color='rgba(255, 0, 0, 0.7)'), row=2, col=1)
-    fig.add_trace(go.Bar(x=plot_data["date_day"], y=plot_data["positive"], name="Positive",
-                         marker_color='rgba(0, 128, 0, 0.7)'), row=2, col=1)
-
-    fig.update_layout(title=f"แนวโน้มอารมณ์ของข่าว + ราคาหุ้น ({symbol})",
-                      barmode="stack", height=650, hovermode="x unified", template="plotly_white")
-    st.plotly_chart(fig, use_container_width=True)
+        # จำนวนข่าว
+        for col in ["neutral", "negative", "positive"]:
+            if col not in plot_data.columns:
+                plot_data[col] = 0
+        fig.add_trace(go.Bar(x=plot_data["date_day"], y=plot_data["neutral"], name="Neutral", marker_color='rgba(128, 128, 128, 0.7)'), row=2, col=1)
+        fig.add_trace(go.Bar(x=plot_data["date_day"], y=plot_data["negative"], name="Negative", marker_color='rgba(255, 0, 0, 0.7)'), row=2, col=1)
+        fig.add_trace(go.Bar(x=plot_data["date_day"], y=plot_data["positive"], name="Positive", marker_color='rgba(0, 128, 0, 0.7)'), row=2, col=1)
+
+        fig.update_layout(title=f"แนวโน้มอารมณ์ของข่าว + ราคาหุ้น ({symbol})", barmode="stack", height=650, hovermode="x unified", template="plotly_white")
+        st.plotly_chart(fig, use_container_width=True)
 
     # แสดงรายการข่าว
     st.subheader("📰 รายการข่าวทั้งหมด")
     st.dataframe(news_df[["date", "source", "text", "sentiment", "theme", "url"]], use_container_width=True)
 
-# --------------------------
+# ---------------------------------------------------------
 # RUN APP
-# --------------------------
+# ---------------------------------------------------------
 if __name__ == "__main__":
     nltk.download("stopwords", quiet=True)
-    main()
+    main()