app.py

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]  # Estrai il dominio
        
        if source_domain in trusted_sources:
            status = "✅ Trusted Source"
        else:
            status = "⚠️ Unverified Source"
        
        categorized_results.append(f"{status}: [{result['title']}]({result['url']}) ({source_domain})")
    
    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)

    # 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",
    outputs="text",
    title="🕵️ Fake News Detective",
    description="Paste a news article or statement and get a credibility analysis"
)

# Avvio dell'app
if __name__ == "__main__":
    interface.launch()