fixed search_tool method

app.py

@@ -1,23 +1,23 @@
 import gradio as gr
 from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
 
-
+# Inizializza il modello e l'agente
 search_tool = DuckDuckGoSearchTool()
 model = HfApiModel(model_id="https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud/")
 agent = CodeAgent(model=model, tools=[search_tool])
 
-
+# Lista delle fonti affidabili
 trusted_sources = [
     "bbc.com", "reuters.com", "apnews.com", "nytimes.com",
     "cnn.com", "forbes.com", "theguardian.com", "npr.org"
 ]
 
-
+# Funzione per classificare le fonti
 def classify_sources(search_results):
     categorized_results = []
     
     for result in search_results:
-        source_domain = result['url'].split('/')[2]  
+        source_domain = result['url'].split('/')[2]  # Estrai il dominio
         
         if source_domain in trusted_sources:
             status = "✅ Trusted Source"
@@ -29,22 +29,22 @@ def classify_sources(search_results):
     return "\n".join(categorized_results)
 
 
-
+# Funzione principale per analizzare le notizie
 def detect_fake_news(news_text):
+    # Cerca informazioni sulla notizia (CORRETTO!)
+    search_results = search_tool.invoke(news_text)
 
-    search_results = search_tool.run(news_text)
-
-
+    # Analizza le fonti
     sources_classification = classify_sources(search_results)
 
-
+    # Interroga il modello LLM
     response = agent.run(f"Check if this news is true or fake: {news_text}")
 
- 
+    # Restituisci il risultato finale con analisi fonti
     return f"{response}\n\n🔍 **Source Analysis:**\n{sources_classification}"
 
 
-
+# Configurazione di Gradio
 interface = gr.Interface(
     fn=detect_fake_news,
     inputs="text",
@@ -53,6 +53,7 @@ interface = gr.Interface(
     description="Paste a news article or statement and get a credibility analysis"
 )
 
-
+# Avvio dell'app
 if __name__ == "__main__":
     interface.launch()
+