app.py

import streamlit as st
import requests
import pandas as pd
from datetime import datetime, timedelta
import nltk
from wordcloud import WordCloud
import base64
from io import BytesIO
import numpy as np
from sklearn.linear_model import LinearRegression
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import yfinance as yf
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# --------------------------
# CONFIG
# --------------------------
st.set_page_config(page_title="📰 News Sentiment Analysis for Young Investor", layout="wide")
API_KEY = "88bc396d4eab4be494a4b86ec842db47"

# --------------------------
# โหลด FinBERT model
# --------------------------
@st.cache_resource
def load_finbert():
    tokenizer = AutoTokenizer.from_pretrained("project-aps/finbert-finetune")
    model = AutoModelForSequenceClassification.from_pretrained("project-aps/finbert-finetune")
    return tokenizer, model

tokenizer, model = load_finbert()

# --------------------------
# UTILITIES
# --------------------------
def analyze_text(text):
    """วิเคราะห์อารมณ์ของข่าว"""
    if not text or not text.strip():
        return 0

    inputs = tokenizer(
        text,
        return_tensors="pt",
        padding=True,
        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])
    return float(score)


def generate_wordcloud(text):
    stopwords = nltk.corpus.stopwords.words('english')
    wordcloud = WordCloud(width=800, height=400, background_color="white", stopwords=stopwords).generate(text)
    buf = BytesIO()
    wordcloud.to_image().save(buf, format="PNG")
    return base64.b64encode(buf.getvalue()).decode()


# --------------------------
# แปลงชื่อ/ตัวย่อ → (Company Name, Symbol)
# --------------------------
def resolve_company_symbol(keyword: str):
    keyword = keyword.strip()
    ticker = None
    name = None

    try:
        data = yf.Ticker(keyword)
        info = data.info
        if "symbol" in info and info["symbol"]:
            ticker = info["symbol"]
            name = info.get("longName", info.get("shortName", keyword))
        else:
            url = f"https://query2.finance.yahoo.com/v1/finance/search?q={keyword}"
            res = requests.get(url).json()
            if "quotes" in res and len(res["quotes"]) > 0:
                q = res["quotes"][0]
                ticker = q.get("symbol")
                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


# --------------------------
# ดึงข่าว 7 วัน
# --------------------------
@st.cache_data(ttl=3600)
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:
        url = (
            f"https://newsapi.org/v2/everything?"
            f"q={query_keyword}&"
            f"from={from_date}&to={to_date}&"
            f"language=en&sortBy=publishedAt&"
            f"pageSize=100&page={page}&apiKey={API_KEY}"
        )
        r = requests.get(url)
        data = r.json()
        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({
                    "date": pd.to_datetime(a["publishedAt"]),
                    "text": f"{a['title']} {a['description']}",
                    "source": a["source"]["name"],
                    "url": a["url"]
                })

        if len(articles) < 100:
            break
        page += 1

    return pd.DataFrame(all_articles)


# --------------------------
# ดึงราคาหุ้น
# --------------------------
@st.cache_data(ttl=3600)
def fetch_stock_price(symbol, start_date, end_date):
    try:
        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()


# --------------------------
# MAIN APP
# --------------------------
def main():
    st.title("📰 News Sentiment Analysis for Young Investor")
    st.markdown("วิเคราะห์แนวโน้มอารมณ์ของข่าวย้อนหลัง 7 วัน พร้อมราคาหุ้น")

    # Sidebar
    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

    # ดึงข่าว
    st.info(f"กำลังดึงข่าวย้อนหลัง 7 วันสำหรับ '{keyword}'...")
    news_df = fetch_financial_news(keyword)
    if news_df.empty:
        st.warning("ไม่พบบทความข่าว")
        return

    # วิเคราะห์ Sentiment
    st.info("กำลังวิเคราะห์อารมณ์ของข่าว...")
    news_df["sentiment"] = news_df["text"].apply(analyze_text)
    news_df["date"] = pd.to_datetime(news_df["date"])

    # Metrics
    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}%")

    # WordCloud
    st.subheader("☁️ Word Cloud")
    all_text = " ".join(news_df["text"].tolist())
    img = generate_wordcloud(all_text)
    st.image(f"data:image/png;base64,{img}", use_column_width=True)

    # ---------------------------------------------------------
    # เตรียมข้อมูลสำหรับกราฟ Sentiment & Price
    # ---------------------------------------------------------
    st.subheader("📈 แนวโน้มอารมณ์ของข่าว & ราคาหุ้น")

    news_df["date_day"] = pd.to_datetime(news_df["date"].dt.date)

    def sentiment_type(score):
        if score > 0.1:
            return "positive"
        if score < -0.1:
            return "negative"
        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")

    if len(df_sorted) < 2:
        st.warning("ข้อมูลไม่พอสร้างแนวโน้ม")
        st.dataframe(news_df)
        return

    # ดึงราคาหุ้น
    _, symbol = resolve_company_symbol(keyword)
    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
    # ---------------------------------------------------------
    correlation = plot_data['price'].corr(plot_data['avg_sentiment'])

    corr_text = "ไม่มีความสัมพันธ์"
    if correlation > 0.5:
        corr_text = "มีความสัมพันธ์ในทิศทางเดียวกัน"
    elif correlation < -0.5:
        corr_text = "มีความสัมพันธ์ในทิศทางตรงกันข้าม"

    st.metric("วิเคราะห์ความสัมพันธ์ระหว่างอารมณ์ของข่าวกับราคาหุ้น (Correlation)", corr_text, f"{correlation:.2f}")

    # ---------------------------------------------------------
    # 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,
            plot_data["timestamp"].max() + future_days + 1
        )
        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="#02a1f7", 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="#02a1f7", 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"แนวโน้มอารมณ์ข่าว + 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", "url"]], use_container_width=True)


# ---------------------------------------------------------
# RUN APP
# ---------------------------------------------------------
if __name__ == "__main__":
    nltk.download("stopwords", quiet=True)
    main()