Update app.py

app.py

@@ -3,28 +3,30 @@ 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  # Import the explanation generator
+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 from .env file
+# Load environment variables
 load_dotenv()
 
-# Constants
+# Initialize database and executor
+db = DatabaseComponent()
+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")
 
-# Initialize ThreadPoolExecutor
-executor = ThreadPoolExecutor(max_workers=3)  # Increased workers to accommodate explanation task
 
-# Function to process input and evaluate truthfulness
+# Function to process and verify news
 def evaluate_news(news_input):
-    # Display loading message
-    yield "**Processing... Please wait while we analyze the information.** ⏳"
+    yield "**Processing... Please wait.** ⏳"
 
     # Handle URL input
     if news_input.startswith("http"):
@@ -37,94 +39,120 @@ def evaluate_news(news_input):
             yield f"**Error processing the URL:** {str(e)}"
             return
     else:
-        # Direct text input
         news_text = news_input
 
     try:
-        # Run both search functions concurrently using ThreadPoolExecutor
+        # 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 the results of both tasks
+        # Wait for results
         kg_content = future_kg.result()
         online_search_results = future_online.result()
 
-        # Combine context from KG and online search
+        # 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'
-        # print(context)  # Debug log
 
-        # Calculate truth score
+        # Compute truth score
         truth_score = calculate_truthfulness_score(info=news_text, context=context)
+        truth_percentage = truth_score * 100  # Convert to percentage
 
-        # Determine truthfulness status and recommendation
+        # Determine truth status
         if truth_score > 0.7:
-            status = "likely true"
-            recommendation = "You can reasonably trust this information, but further verification is always recommended for critical decisions."
+            status = f"**{truth_percentage:.0f}% chances to be true** - This news is likely true."
         elif truth_score > 0.4:
-            status = "uncertain"
-            recommendation = "This information might be partially true, but additional investigation is required before accepting it as fact."
+            status = f"**{truth_percentage:.0f}% chances to be true** - This news is uncertain. Please verify further."
         else:
-            status = "unlikely to be true"
-            recommendation = "It is recommended to verify this information through multiple reliable sources before trusting it."
+            status = f"**{truth_percentage:.0f}% chances to be true** - This news is unlikely to be true. Proceed with caution."
+
+        # Save result in database
+        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"
 
-        # Display initial result with score
-        result = f"**News**: \"{news_text[:300]}...\"\n\n"
-        result += f"**Truthfulness Score**: {truth_score:.2f} (**{status.capitalize()}**)\n\n"
-        result += f"**Analysis**: {recommendation}\n\n"
-        yield result  # Immediately display score and recommendation
+        yield result  # Display initial results
 
         # Generate explanation asynchronously
         future_explanation = executor.submit(generate_explanation, news_text, context, truth_score)
+        explanation = future_explanation.result()
 
-        # Add explanation and sources once available
-        explanation = future_explanation.result()  # Wait for explanation result
         if explanation:
-            result += f"**Explanation**: {explanation}\n\n"  # Append explanation
+            result += f"**Explanation:** {explanation}\n\n"
 
-            # Add sources from the online search results (top 5 sources)
-            sources = online_search_results.get('sources', [])
-            if sources:
-                result += "\n**Sources**:\n"
-                # Ensure we only show up to 5 sources
-                for i, source in enumerate(sources[:5]):
-                    result += f"{i + 1}. {source}\n"
-                result += "\n*Please make sure to do your own research for more confirmation and to cross-check the information.*"
+        # 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  # Update UI with explanation and sources
+        yield result  # Final output with sources
 
     except Exception as e:
-        yield f"**Error occurred while processing the input:** {str(e)}"
+        yield f"**Error:** {str(e)}"
+
+
+# Function to fetch dashboard data
+def fetch_dashboard_data():
+    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() as demo:
-    gr.Markdown("# 📰 EchoTruth: Verify News Authenticity in Real-Time")
-    
-    gr.Markdown("""
-    **How to use:**
-    1. Enter a news article or URL in the box below.
-    2. Click on **Check Truthfulness**.
-    3. Receive a **truthfulness score** along with **explanations and sources** to help you assess the authenticity of the content.
-    """)
-    
-    with gr.Row():
-        input_box = gr.Textbox(
-            placeholder="Enter news text or URL here... (e.g., https://example.com)",
-            label="Input News or URL",
-            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.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()
+            refresh_btn = gr.Button("🔄 Refresh Data")
+            refresh_btn.click(fn=fetch_dashboard_data, inputs=[], outputs=[dashboard_output])
+
+    # Automatically refresh dashboard when the tab is opened
+    tabs.select(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. We recommend checking multiple sources before making decisions based on news.")
-    
-demo.launch()
+    gr.Markdown("EchoTruth uses AI to help users verify news authenticity in real-time.")
+
+demo.launch()