import gradio as gr
from newspaper import Article
from modules.online_search import search_online
from modules.validation import calculate_truthfulness_score
from modules.knowledge_graph import search_kg
from modules.generate_explanation import generate_explanation
from dotenv import load_dotenv
import os
from concurrent.futures import ThreadPoolExecutor
from modules.record import DatabaseComponent  # Import DatabaseComponent

# Load environment variables
load_dotenv()

# Initialize database (handle connection failures)
db = None
try:
    db = DatabaseComponent()
except Exception as e:
    print(f"[ERROR] Database connection failed: {str(e)}")

# Initialize thread executor
executor = ThreadPoolExecutor(max_workers=3)

# Constants for file paths and API keys
KG_INDEX_PATH = "KG/news_category_index.faiss"
KG_DATASET_PATH = "KG/News_Category_Dataset_v3.json"
SEARCH_API_KEY = os.getenv("SEARCH_API_KEY")
SEARCH_BASE_URL = os.getenv("SEARCH_BASE_URL")
SEARCH_MODEL = os.getenv("SEARCH_MODEL")


# Function to process and verify news
def evaluate_news(news_input):
    yield "**Processing... Please wait.** ⏳"

    # Handle URL input
    if news_input.startswith("http"):
        try:
            article = Article(news_input)
            article.download()
            article.parse()
            news_text = article.title + ". " + article.text
        except Exception as e:
            yield f"**Error processing the URL:** {str(e)}"
            return
    else:
        news_text = news_input

    try:
        # Run search tasks concurrently
        future_kg = executor.submit(search_kg, news_text, KG_INDEX_PATH, KG_DATASET_PATH)
        future_online = executor.submit(search_online, news_text, SEARCH_API_KEY, SEARCH_BASE_URL, SEARCH_MODEL)

        # Wait for results
        kg_content = future_kg.result()
        online_search_results = future_online.result()

        # Extract citations from the search results
        citations = online_search_results.get("citations", [])  # List of sources
        first_citation = citations[0] if citations else "N/A"  # Store first citation in DB

        # Combine context
        context = online_search_results['message_content'] + '\n' + kg_content + '\n' + 'Device set to use cpu'

        # Compute truth score
        truth_score = calculate_truthfulness_score(info=news_text, context=context)
        truth_percentage = truth_score * 100  # Convert to percentage

        # Determine truth status
        if truth_score > 0.7:
            status = f"**{truth_percentage:.0f}% chances to be true** - This news is likely true."
        elif truth_score > 0.4:
            status = f"**{truth_percentage:.0f}% chances to be true** - This news is uncertain. Please verify further."
        else:
            status = f"**{truth_percentage:.0f}% chances to be true** - This news is unlikely to be true. Proceed with caution."

        # Save result in database if connection is available
        if db is not None:
            db.save_news_verification(news_text[:100], truth_score, first_citation)

        # Initial result
        result = f"**News:** \"{news_text[:300]}...\"\n\n"
        result += f"**Truthfulness Score:** {truth_score:.2f} ({status})\n\n"

        yield result  # Display initial results

        # Generate explanation asynchronously
        future_explanation = executor.submit(generate_explanation, news_text, context, truth_score)
        explanation = future_explanation.result()

        if explanation:
            result += f"**Explanation:** {explanation}\n\n"

        # Display sources
        if citations:
            result += "\n**Sources & References:**\n"
            for i, source in enumerate(citations[:5]):  # Show up to 5 sources
                result += f"{i + 1}. [{source}]({source})\n"

        yield result  # Final output with sources

    except Exception as e:
        yield f"**Error:** {str(e)}"


# Function to fetch dashboard data
def fetch_dashboard_data():
    if db is None:
        return "**⚠️ Database unavailable. Recent verification records cannot be displayed.**"

    total_news = db.get_total_news_count()
    last_10_news = db.get_last_10_news()

    # Generate table-style layout for recent verifications
    dashboard_info = f"**Total News Verified:** {total_news}\n\n"

    if last_10_news:
        table = "| # | News Title | Score (%) | Date Verified | Citation |\n"
        table += "|---|------------|-----------|--------------|----------|\n"

        for i, news in enumerate(last_10_news, start=1):
            truth_percentage = news['score'] * 100  # Convert to percentage
            citation = f"[Source]({news['citation']})" if news['citation'] != "N/A" else "N/A"
            table += f"| {i} | {news['title'][:50]}... | {truth_percentage:.0f}% | {news['timestamp']} | {citation} |\n"

        dashboard_info += table
    else:
        dashboard_info += "_No records found._"

    return dashboard_info


# Gradio Interface
with gr.Blocks(css="""
    .gradio-container { font-family: 'Georgia', serif; font-size: 16px; }
    h1, h2, h3 { font-family: 'Times New Roman', serif; }
    table { width: 100%; border-collapse: collapse; }
    th, td { padding: 10px; border: 1px solid #ddd; text-align: left; }
""") as demo:
    with gr.Tabs() as tabs:
        with gr.Tab("🔍 Verify News"):
            gr.Markdown("# 📰 EchoTruth: News Verification")
            gr.Markdown("""
            **How it Works:**
            - Enter a news article **or** a URL.
            - Click **Check Truthfulness**.
            - Get a **truth score**, an explanation, and references.
            """)

            input_box = gr.Textbox(placeholder="Paste news text or URL...", label="News Input", lines=5)
            submit_btn = gr.Button("Check Truthfulness")
            output_box = gr.Markdown()
            submit_btn.click(fn=evaluate_news, inputs=[input_box], outputs=[output_box])

        with gr.Tab("📊 Dashboard") as dashboard_tab:
            gr.Markdown("# 📊 Verification Dashboard")
            dashboard_output = gr.Markdown("_Click 'Refresh Data' to load latest records._")
            refresh_btn = gr.Button("🔄 Refresh Data")
            refresh_btn.click(fn=fetch_dashboard_data, inputs=[], outputs=[dashboard_output])

    gr.Markdown("### **About EchoTruth**")
    gr.Markdown("EchoTruth uses AI to help users verify news authenticity in real-time.")

demo.launch()