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'])

    # หาข้อความอธิบาย (ข้อความใหญ่ด้านบน)
    if correlation > 0.75:
        corr_label = "มีความสัมพันธ์กันอย่างมากในทิศทางเดียวกัน"
    elif correlation > 0.5:
        corr_label = "มีความสัมพันธ์กันปานกลางในทิศทางเดียวกัน"
    elif correlation > 0.25:
        corr_label = "มีความสัมพันธ์กันเล็กน้อยในทิศทางเดียวกัน"
    elif correlation < -0.75:
        corr_label = "มีความสัมพันธ์กันอย่างมากในทิศทางตรงกันข้าม"
    elif correlation < -0.5:
        corr_label = "มีความสัมพันธ์กันปานกลางในทิศทางตรงกันข้าม"
    elif correlation < -0.25:
        corr_label = "มีความสัมพันธ์กันเล็กน้อยในทิศทางตรงกันข้าม"
    else:
        corr_label = "ไม่มีความสัมพันธ์กัน"

    corr_value_text = f"Correlation = {correlation:.2f}"

    st.metric(
        "วิเคราะห์ความสัมพันธ์ระหว่างอารมณ์ของข่าวกับราคาหุ้น (Correlation)",
        corr_label,                 # ตัวบน (ใหญ่)
        corr_value_text             # ตัวล่าง (สีเขียว/แดง)
    )

    # ---------------------------------------------------------
    # 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()