Update app.py

app.py

@@ -45,6 +45,7 @@ def analyze_text(text):
     """วิเคราะห์อารมณ์ของข่าวด้วย FinBERT"""
     if not text or not text.strip():
         return 0
+
     inputs = tokenizer(
         text,
         return_tensors="pt",
@@ -52,14 +53,17 @@ def analyze_text(text):
         truncation=True,
         max_length=512
     )
+
     with torch.no_grad():
         outputs = model(**inputs)
         logits = outputs.logits
         probs = torch.softmax(logits, dim=1).numpy()[0]
-        # FinBERT = [negative, neutral, positive]
-        score = (-1 * probs[0]) + (0 * probs[1]) + (1 * probs[2])
+
+    # FinBERT = [negative, neutral, positive]
+    score = (-1 * probs[0]) + (0 * probs[1]) + (1 * probs[2])
     return float(score)
 
+
 def summarize_themes(news_texts):
     """สรุปธีมข่าวด้วย Zero-shot classification"""
     themes = []
@@ -77,6 +81,7 @@ def resolve_company_symbol(keyword: str):
     keyword = keyword.strip()
     ticker = None
     name = None
+
     try:
         data = yf.Ticker(keyword)
         info = data.info
@@ -92,10 +97,12 @@ def resolve_company_symbol(keyword: str):
                 name = q.get("longname", q.get("shortname", keyword))
     except:
         pass
+
     if not ticker:
         ticker = keyword.upper()
     if not name:
         name = keyword.capitalize()
+
     return name, ticker
 
 # --------------------------
@@ -106,7 +113,9 @@ def fetch_financial_news(keyword):
     company, symbol = resolve_company_symbol(keyword)
     to_date = datetime.now().strftime('%Y-%m-%d')
     from_date = (datetime.now() - timedelta(days=7)).strftime('%Y-%m-%d')
+
     query_keyword = f"({company} OR {symbol}) finance stock"
+
     all_articles = []
     page = 1
     while True:
@@ -122,9 +131,11 @@ def fetch_financial_news(keyword):
         if data.get("status") != "ok":
             st.error(f"API Error: {data}")
             break
+
         articles = data.get("articles", [])
         if not articles:
             break
+
         for a in articles:
             if a["description"]:
                 all_articles.append({
@@ -133,9 +144,11 @@ def fetch_financial_news(keyword):
                     "source": a["source"]["name"],
                     "url": a["url"]
                 })
+
         if len(articles) < 100:
             break
         page += 1
+
     return pd.DataFrame(all_articles)
 
 # --------------------------
@@ -147,14 +160,18 @@ def fetch_stock_price(symbol, start_date, end_date):
         start_str = (start_date - timedelta(days=2)).strftime('%Y-%m-%d')
         end_str = (end_date + timedelta(days=1)).strftime('%Y-%m-%d')
         df = yf.download(symbol, start=start_str, end=end_str, interval="1d")
+
         if df.empty:
             st.warning("ไม่พบข้อมูลราคาหุ้น")
             return pd.DataFrame()
+
         df = df.reset_index()
         df_subset = df[['Date', 'Close']]
         df_subset.columns = ['date', 'price']
         df_subset["date"] = pd.to_datetime(df_subset["date"].dt.date)
+
         return df_subset
+
     except Exception as e:
         st.warning(f"ดึงราคาหุ้นล้มเหลว: {e}")
         return pd.DataFrame()
@@ -178,7 +195,6 @@ def main():
     # ดึงข่าว
     st.info(f"กำลังดึงข่าวย้อนหลัง 7 วันสำหรับ '{keyword}'...")
     news_df = fetch_financial_news(keyword)
-
     if news_df.empty:
         st.warning("ไม่พบบทความข่าว")
         return
@@ -210,6 +226,7 @@ def main():
     # ส่วนกราฟ Sentiment & Price (เหมือนเดิม)
     # ---------------------------------------------------------
     st.subheader("📈 แนวโน้มอารมณ์ของข่าว & ราคาหุ้น")
+
     news_df["date_day"] = pd.to_datetime(news_df["date"].dt.date)
 
     def sentiment_type(score):
@@ -223,6 +240,7 @@ def main():
 
     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")
 
     if len(df_sorted) < 2:
@@ -245,7 +263,7 @@ def main():
         corr_text = "มีความสัมพันธ์ในทิศทางเดียวกัน"
     elif correlation < -0.5:
         corr_text = "มีความสัมพันธ์ในทิศทางตรงข้าม"
-    st.metric("วิเคราะห์ความสัมพันธ์ระหว่างอารมณ์ของข่าวกับราคาหุ้น (Correlation)", corr_text, f"{correlation:.2f}")
+    st.metric("วิเคราะห์ความสัมพันธ์ระหว่างอารมณ์ของข่าวกับราคาหุ้น", corr_text, f"{correlation:.2f}")
 
     # Forecast Sentiment
     plot_data["timestamp"] = (plot_data["date_day"] - plot_data["date_day"].min()).dt.days
@@ -263,36 +281,47 @@ 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)
-
-        # 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 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)
-
+    # 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)
+    # 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)
         # 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)
-
-        # 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)
+        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)
 
     # แสดงรายการข่าว
     st.subheader("📰 รายการข่าวทั้งหมด")
@@ -303,4 +332,4 @@ def main():
 # ---------------------------------------------------------
 if __name__ == "__main__":
     nltk.download("stopwords", quiet=True)
-    main()
+    main()