app.py

import pandas as pd
import matplotlib.pyplot as plt
import io
import base64
import os
import traceback
import random
import networkx as nx
from datetime import datetime, timedelta
import gradio as gr
import logging
from jinja2 import Template
from matplotlib import font_manager
# ===== Fonts and Styles =====
# Load local SimHei font if available
simhei_path = 'SimHei.ttf' # Assuming it's .ttf; change to .tiff if needed (though .ttf is standard)
if os.path.exists(simhei_path):
    font_prop = font_manager.FontProperties(fname=simhei_path)
    plt.rcParams['font.family'] = 'sans-serif'
    plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft JhengHei', 'Noto Sans TC', 'Arial Unicode MS']
else:
    plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei', 'Noto Sans TC', 'SimHei', 'Arial Unicode MS']
plt.rcParams['axes.unicode_minus'] = False
plt.style.use("seaborn-v0_8")
# ===== Logging =====
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# ===== Parameters =====
candidates = ["Hsu Chih-chieh", "Chiu Yi-ying", "Lai Jui-lung", "Lin Dai-hua", "Ko Chih-en"]
days_back = 7
max_tweets_per_candidate = 20
news_file = "news_sample.csv"
history_file = "history_sentiment.csv"
# ===== Sentiment Analysis =====
try:
    from transformers import pipeline
    sentiment_pipeline = pipeline(
        "sentiment-analysis",
        model="lxyuan/distilbert-base-multilingual-cased-sentiments-student"
    )
    def sentiment(text):
        return sentiment_pipeline(text)[0]
except:
    def sentiment(text):
        return {
            "label": random.choice(["positive", "negative", "neutral"]),
            "score": random.uniform(0.3, 0.9)
        }
# ===== Simulate Post Fetching =====
def fetch_tweets(candidate):
    sample_texts = {
        "Hsu Chih-chieh": ["Hsu Chih-chieh actively participates in local activities", "Hsu Chih-chieh criticized for empty policies", "Support Hsu Chih-chieh, build a new future for Kaohsiung!"],
        "Chiu Yi-ying": ["Chiu Yi-ying strongly states intention to run for mayor", "Chiu Yi-ying criticizes Lin Dai-hua", "Chiu Yi-ying promotes Hakka culture"],
        "Lai Jui-lung": ["Lai Jui-lung promotes marine economy", "Lai Jui-lung leads in polls", "Lai Jui-lung questioned for lack of experience"],
        "Lin Dai-hua": ["Lin Dai-hua actively engages with grassroots", "Lin Dai-hua involved in assistant fee controversy", "Lin Dai-hua receives support from Zheng Guohui"],
        "Ko Chih-en": ["Ko Chih-en leads significantly in polls", "Ko Chih-en criticized for missing disaster inspection", "Ko Chih-en promotes youth policies"]
    }
    return pd.DataFrame([
        {
            "Date": datetime.now() - timedelta(days=random.randint(0, days_back - 1)),
            "User": f"user{random.randint(1, 100)}",
            "Content": random.choice(sample_texts.get(candidate, [f"{candidate}'s post {i}"])),
            "Candidate": candidate
        }
        for i in range(random.randint(5, max_tweets_per_candidate))
    ])
# ===== Tool: Matplotlib to base64 =====
def fig_to_base64():
    buf = io.BytesIO()
    plt.savefig(buf, format="png", dpi=120, bbox_inches="tight")
    buf.seek(0)
    img_b64 = base64.b64encode(buf.read()).decode("utf-8")
    buf.close()
    plt.close()
    return img_b64
# ===== Multi-Chart Generator =====
def generate_charts(all_df, summary, df_hist):
    results = {}
    # 1. Daily Sentiment Ratios
    fig = plt.figure(figsize=(8, 5))
    summary[['Positive Ratio', 'Negative Ratio', 'Neutral Ratio']].plot(
        kind='bar', stacked=True, colormap='coolwarm', ax=fig.gca()
    )
    plt.title("Candidates' Daily Social Sentiment Ratios")
    plt.ylabel("Ratio")
    plt.xlabel("Candidate")
    plt.legend(["Positive", "Negative", "Neutral"])
    results["img_b64_today"] = fig_to_base64()
    # 2. Historical Sentiment Trends
    fig = plt.figure(figsize=(10, 5))
    for c in candidates:
        temp = df_hist[df_hist['Candidate'] == c]
        if not temp.empty:
            plt.plot(temp['Date'], temp['Positive Ratio'], marker='o', label=f"{c} Positive")
            plt.plot(temp['Date'], temp['Negative Ratio'], marker='x', label=f"{c} Negative")
            plt.plot(temp['Date'], temp['Neutral Ratio'], marker='s', label=f"{c} Neutral")
    plt.title("Candidates' Historical Sentiment Trends")
    plt.xticks(rotation=45)
    plt.ylabel("Ratio")
    plt.xlabel("Date")
    plt.legend()
    results["img_b64_trend"] = fig_to_base64()
    # 3. Social Sentiment Trends
    sentiment_trend = all_df.groupby([pd.Grouper(key='Date', freq='D'), 'Sentiment']).size().unstack(fill_value=0)
    sentiment_trend = sentiment_trend.div(sentiment_trend.sum(axis=1), axis=0).fillna(0)
    fig = plt.figure(figsize=(8, 5))
    for s in ['positive', 'negative', 'neutral']:
        if s in sentiment_trend.columns:
            plt.plot(sentiment_trend.index, sentiment_trend[s], marker='o', label={'positive':'Positive', 'negative':'Negative', 'neutral':'Neutral'}[s])
    plt.title("Social Sentiment Trends")
    plt.xlabel("Date")
    plt.ylabel("Ratio")
    plt.legend()
    results["img_social_sentiment"] = fig_to_base64()
    # 4. Platform Performance
    platforms = ["X", "Facebook", "Instagram", "PTT", "Line"]
    platform_counts = pd.Series({p: random.randint(10, 100) for p in platforms})
    fig = plt.figure(figsize=(8, 5))
    plt.bar(platforms, platform_counts, color='skyblue')
    plt.title("Platform Post Volumes")
    plt.xlabel("Platform")
    plt.ylabel("Post Count")
    results["img_platform_performance"] = fig_to_base64()
    # 5. Candidates' Volume Trends
    candidate_trend = all_df.groupby([pd.Grouper(key='Date', freq='D'), 'Candidate']).size().unstack(fill_value=0)
    fig = plt.figure(figsize=(8, 5))
    for c in candidates:
        if c in candidate_trend.columns:
            plt.plot(candidate_trend.index, candidate_trend[c], marker='o', label=c)
    plt.title("Candidates' Post Volume Trends")
    plt.xlabel("Date")
    plt.ylabel("Post Count")
    plt.legend()
    results["img_candidate_volume"] = fig_to_base64()
    # 6. Candidates' Sentiment Analysis
    fig = plt.figure(figsize=(8, 5))
    summary[['Positive Ratio', 'Negative Ratio', 'Neutral Ratio']].plot(
        kind='bar', stacked=True, colormap='coolwarm', ax=fig.gca()
    )
    plt.title("Candidates' Post Sentiment Analysis (Positive/Negative/Neutral)")
    plt.ylabel("Ratio")
    plt.xlabel("Candidate")
    plt.legend(["Positive", "Negative", "Neutral"])
    results["img_candidate_sentiment"] = fig_to_base64()
    # 7. Knowledge Graph
    fig, ax = plt.subplots(figsize=(8, 6))
    G = nx.Graph()
    for c in candidates:
        G.add_node(c)
    for i in range(len(candidates) - 1):
        G.add_edge(candidates[i], candidates[i + 1])
    nx.draw(G, nx.spring_layout(G), with_labels=True, node_color='lightgreen', font_size=12, ax=ax)
    plt.title("Candidates' Knowledge Graph")
    results["img_knowledge_graph"] = fig_to_base64()
    return results
# ===== Main Analysis Function =====
def run_analysis():
    try:
        # Embed the template as a string to avoid file dependency and ensure syntax is correct
        html_template = """
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>2026 Kaohsiung Mayoral Election Public Opinion Analysis Report</title>
    <script src="https://cdn.tailwindcss.com"></script>
</head>
<body class="bg-gray-100 font-sans leading-normal tracking-normal">
    <div class="container mx-auto p-4">
        <h1 class="text-3xl font-bold mb-4">2026 Kaohsiung Mayoral Election Public Opinion Analysis Report</h1>
        <p class="mb-4">Report Date: {{ report_date }}</p>
       
        <h2 class="text-2xl font-bold mb-2">Engagement Summary</h2>
        {{ engagement_table | safe }}
       
        <h2 class="text-2xl font-bold mb-2">News Summary</h2>
        <ul class="list-disc pl-5 mb-4">
            {% for key, value in news_summary %}
            <li><strong>{{ key }}</strong>: {{ value }}</li>
            {% endfor %}
        </ul>
       
        <h2 class="text-2xl font-bold mb-2">News Details</h2>
        {{ news_table | safe }}
       
        <h2 class="text-2xl font-bold mb-2">Today's Sentiment Ratios</h2>
        <img src="data:image/png;base64,{{ img_b64_today }}" alt="Today's Sentiment Ratios" class="mb-4">
       
        <h2 class="text-2xl font-bold mb-2">Historical Sentiment Trends</h2>
        <img src="data:image/png;base64,{{ img_b64_trend }}" alt="Historical Sentiment Trends" class="mb-4">
       
        <h2 class="text-2xl font-bold mb-2">Social Sentiment Trends</h2>
        <img src="data:image/png;base64,{{ img_social_sentiment }}" alt="Social Sentiment Trends" class="mb-4">
       
        <h2 class="text-2xl font-bold mb-2">Platform Performance</h2>
        <img src="data:image/png;base64,{{ img_platform_performance }}" alt="Platform Performance" class="mb-4">
       
        <h2 class="text-2xl font-bold mb-2">Candidates' Volume Trends</h2>
        <img src="data:image/png;base64,{{ img_candidate_volume }}" alt="Candidates' Volume Trends" class="mb-4">
       
        <h2 class="text-2xl font-bold mb-2">Candidates' Sentiment Analysis</h2>
        <img src="data:image/png;base64,{{ img_candidate_sentiment }}" alt="Candidates' Sentiment Analysis" class="mb-4">
       
        <h2 class="text-2xl font-bold mb-2">Knowledge Graph</h2>
        <img src="data:image/png;base64,{{ img_knowledge_graph }}" alt="Knowledge Graph" class="mb-4">
    </div>
</body>
</html>
        """
        # --- Posts & Sentiment Analysis ---
        all_df = pd.concat([fetch_tweets(c) for c in candidates], ignore_index=True)
        all_df['Sentiment'] = all_df['Content'].apply(lambda x: sentiment(x)['label'])
        all_df['Confidence'] = all_df['Content'].apply(lambda x: sentiment(x)['score'])
        # --- Statistics ---
        summary = all_df.groupby(['Candidate', 'Sentiment']).size().unstack(fill_value=0)
        summary['Total Posts'] = summary.sum(axis=1)
        summary['Positive Ratio'] = summary.get('positive', 0) / summary['Total Posts'].replace(0, 1)
        summary['Negative Ratio'] = summary.get('negative', 0) / summary['Total Posts'].replace(0, 1)
        summary['Neutral Ratio'] = summary.get('neutral', 0) / summary['Total Posts'].replace(0, 1)
        # --- Historical Data ---
        today_str = datetime.now().strftime('%Y-%m-%d')
        hist_row = summary[['Positive Ratio', 'Negative Ratio', 'Neutral Ratio']].copy()
        hist_row['Date'] = today_str
        hist_row['Candidate'] = summary.index
        df_hist = pd.concat(
            [pd.read_csv(history_file), hist_row],
            ignore_index=True
        ) if os.path.exists(history_file) else hist_row
        df_hist.to_csv(history_file, index=False)
        # --- Charts ---
        charts = generate_charts(all_df, summary, df_hist)
        # --- News ---
        if os.path.exists(news_file):
            df_news = pd.read_csv(news_file)
            news_summary = df_news.groupby('Category').size().to_dict()
            news_table = df_news.to_html(index=False, classes="min-w-full border border-gray-200")
        else:
            news_summary = {
                "Polls": "Ko Chih-en leads Green Camp candidates in multiple polls.",
                "Intra-party Competition": "Intense competition in the DPP primary.",
                "Controversy": "Lin Dai-hua involved in assistant fee controversy."
            }
            news_table = "<p>No news data available</p>"
        # Convert news_summary to list of tuples to support iteration in template
        news_summary = list(news_summary.items())
        # --- Engagement Table ---
        engagement_table = f"""
        <table class="min-w-full bg-white border border-gray-200">
            <tr class="bg-gray-100 border-b">
                <th class="py-2 px-4 border-r">Total Engagement</th>
                <td class="py-2 px-4 border-r">{len(all_df)}</td>
                <th class="py-2 px-4 border-r">Positive Sentiment Ratio</th>
                <td class="py-2 px-4 border-r">{all_df['Sentiment'].value_counts(normalize=True).get('positive', 0):.1%}</td>
                <th class="py-2 px-4 border-r">Average Interaction Rate</th>
                <td class="py-2 px-4 border-r">3.9%</td>
                <th class="py-2 px-4 border-r">Active Platforms</th>
                <td class="py-2 px-4">{5}</td>
            </tr>
        </table>
        """
        # --- HTML Rendering ---
        template = Template(html_template)
        html_content = template.render(
            report_date=datetime.now().strftime('%Y-%m-%d %H:%M'),
            engagement_table=engagement_table if engagement_table else "<p>No engagement data provided</p>",
            news_summary=news_summary if news_summary else "<p>No news summary provided</p>",
            news_table=news_table if news_table else "<p>No news data provided</p>",
            **charts
        )
      
        return html_content
    except Exception:
        return f"<pre>❌ Analysis failed:\n{traceback.format_exc()}</pre>"
# ===== Gradio Frontend =====
if __name__ == "__main__":
    iface = gr.Interface(
        fn=run_analysis,
        inputs=[],
        outputs=gr.HTML(),
        title="2026 Kaohsiung Mayoral Election Public Opinion Analysis"
    )
    iface.launch(server_name="0.0.0.0", server_port=7860)