Update app.py

app.py

@@ -1,101 +1,101 @@
-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 dotenv import load_dotenv
-import os
-
-# Load environment variables from .env file
-load_dotenv()
-
-
-# Constants
-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 input and evaluate truthfulness
-def evaluate_news(news_input):
-    # Display loading message
-    yield "**Processing... Please wait while we analyze the information.** ⏳"
-
-    # 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:
-        # Direct text input
-        news_text = news_input
-
-    try:
-        # Retrieve relevant info from FAISS knowledge graph
-        kg_content = search_kg(query=news_text, index_path=KG_INDEX_PATH, dataset_path=KG_DATASET_PATH)
-
-        # Search online for additional context
-        online_search_results = search_online(
-            query=news_text,
-            api_key=SEARCH_API_KEY,
-            base_url=SEARCH_BASE_URL,
-            model=SEARCH_MODEL
-        )
-
-        # Combine context from KG and online search
-        context = kg_content + '\n' + online_search_results['message_content']
-
-        # Calculate truth score
-        truth_score = calculate_truthfulness_score(info=news_text, context=context)
-
-        # Generate explanation based on score
-        if truth_score > 0.7:
-            status = "likely true"
-            recommendation = "You can reasonably trust this information, but further verification is always recommended for critical decisions."
-        elif truth_score > 0.4:
-            status = "uncertain"
-            recommendation = "This information might be partially true, but additional investigation is required before accepting it as fact."
-        else:
-            status = "unlikely to be true"
-            recommendation = "It is recommended to verify this information through multiple reliable sources before trusting it."
-
-        # Final Output
-        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
-
-    except Exception as e:
-        yield f"**Error occurred while processing the input:** {str(e)}"
-
-
-# Gradio Interface
-with gr.Blocks() as demo:
-    gr.Markdown("# 📰 EchoTruth: Amplifying Authenticity in Live Broadcasts")
-    gr.Markdown("### Enter a piece of news or a URL below to validate its authenticity.")
-
-    # Input options
-    with gr.Row():
-        input_box = gr.Textbox(placeholder="Enter news text or URL here...", label="Input News or URL")
-
-    # Output display
-    output_box = gr.Markdown()
-
-    # Submit button
-    submit_btn = gr.Button("Check Truthfulness")
-
-    # Trigger function with spinner
-    submit_btn.click(
-        fn=evaluate_news,
-        inputs=[input_box],
-        outputs=[output_box]
-    )
-
-# Launch Gradio App
+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 dotenv import load_dotenv
+import os
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+# Constants
+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 input and evaluate truthfulness
+def evaluate_news(news_input):
+    # Display loading message
+    yield "**Processing... Please wait while we analyze the information.** ⏳"
+
+    # 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:
+        # Direct text input
+        news_text = news_input
+
+    try:
+        # Retrieve relevant info from FAISS knowledge graph
+        kg_content = search_kg(query=news_text, index_path=KG_INDEX_PATH, dataset_path=KG_DATASET_PATH)
+
+        # Search online for additional context
+        online_search_results = search_online(
+            query=news_text,
+            api_key=SEARCH_API_KEY,
+            base_url=SEARCH_BASE_URL,
+            model=SEARCH_MODEL
+        )
+
+        # Combine context from KG and online search
+        context = online_search_results['message_content'] + '\n' + kg_content
+
+        # Calculate truth score
+        truth_score = calculate_truthfulness_score(info=news_text, context=context)
+
+        # Generate explanation based on score
+        if truth_score > 0.7:
+            status = "likely true"
+            recommendation = "You can reasonably trust this information, but further verification is always recommended for critical decisions."
+        elif truth_score > 0.4:
+            status = "uncertain"
+            recommendation = "This information might be partially true, but additional investigation is required before accepting it as fact."
+        else:
+            status = "unlikely to be true"
+            recommendation = "It is recommended to verify this information through multiple reliable sources before trusting it."
+
+        # Final Output
+        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
+
+    except Exception as e:
+        yield f"**Error occurred while processing the input:** {str(e)}"
+
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 📰 EchoTruth: Amplifying Authenticity in Live Broadcasts")
+    gr.Markdown("### Enter a piece of news or a URL below to validate its authenticity.")
+
+    # Input options
+    with gr.Row():
+        input_box = gr.Textbox(placeholder="Enter news text or URL here...", label="Input News or URL")
+
+    # Output display
+    output_box = gr.Markdown()
+
+    # Submit button
+    submit_btn = gr.Button("Check Truthfulness")
+
+    # Trigger function with spinner
+    submit_btn.click(
+        fn=evaluate_news,
+        inputs=[input_box],
+        outputs=[output_box]
+    )
+
+# Launch Gradio App
 demo.launch()