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import gradio as gr |
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import json |
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import os |
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import requests |
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from bs4 import BeautifulSoup |
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import spacy |
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from transformers import pipeline |
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from datetime import datetime |
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nlp = spacy.load("en_core_web_sm") |
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summarizer = pipeline("summarization", model="facebook/bart-large-cnn") |
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class ConversationalUBRA: |
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def __init__(self): |
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self.conversation_history = [] |
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self.sources = { |
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'duckduckgo': True, |
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'wikipedia': True, |
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'newsapi': False, |
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'google_scholar': False |
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} |
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def analyze_intent(self, query): |
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"""Analizza l'intento della query""" |
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doc = nlp(query) |
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intents = { |
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'information_request': any(token.pos_ in ['NOUN', 'PROPN'] for token in doc), |
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'comparison': any(word in query for word in ['vs', 'comparare', 'confrontare']), |
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'definition': any(word in query for word in ['cos\'è', 'significa', 'definizione']), |
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'how_to': any(word in query for word in ['come', 'funziona', 'procedura']), |
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'opinion': any(word in query for word in ['opinione', 'credi', 'pensiero']) |
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} |
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primary_intent = max(intents, key=intents.get) if any(intents.values()) else 'general' |
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return { |
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'primary': primary_intent, |
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'keywords': [token.lemma_.lower() for token in doc if not token.is_stop] |
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} |
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def collect_information(self, query, intent): |
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"""Raccolta dati da fonti attive""" |
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data_sources = [] |
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if self.sources['duckduckgo']: |
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data_sources.extend(self.search_duckduckgo(query)) |
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if self.sources['wikipedia']: |
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data_sources.extend(self.search_wikipedia(query)) |
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if self.sources['newsapi']: |
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data_sources.extend(self.search_newsapi(query)) |
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if self.sources['google_scholar']: |
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data_sources.extend(self.search_google_scholar(query)) |
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return data_sources |
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def search_duckduckgo(self, query): |
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"""Ricerca su DuckDuckGo""" |
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try: |
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url = f"https://duckduckgo.com/html?q={query}" |
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headers = {'User-Agent': 'Mozilla/5.0'} |
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response = requests.get(url, headers=headers, timeout=10) |
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soup = BeautifulSoup(response.text, 'html.parser') |
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results = [] |
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for item in soup.select('.result__body')[:3]: |
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title = item.select_one('.result__title').get_text(strip=True) |
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snippet = item.select_one('.result__snippet').get_text(strip=True) |
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link = item.select_one('.result__url').get_text(strip=True) |
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results.append(f"🌐 DuckDuckGo:\n{title}\n{snippet}\nLink: {link}\n") |
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return results |
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except Exception as e: |
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return [f"⚠️ Errore DuckDuckGo: {str(e)}"] |
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def search_wikipedia(self, query): |
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"""Ricerca su Wikipedia""" |
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try: |
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url = f"https://it.wikipedia.org/w/api.php?action=query&list=search&srsearch={query}&format=json&srlimit=3" |
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response = requests.get(url, timeout=10) |
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data = response.json() |
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results = [] |
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if 'query' in data and 'search' in data['query']: |
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for item in data['query']['search'][:3]: |
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title = item['title'] |
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snippet = item['snippet'].replace('<span class="searchmatch">', '').replace('</span>', '') |
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page_url = f"https://it.wikipedia.org/wiki/{title.replace(' ', '_')}" |
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results.append(f"📚 Wikipedia:\n{title}\n{snippet}\nLink: {page_url}\n") |
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return results |
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except Exception as e: |
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return [f"⚠️ Errore Wikipedia: {str(e)}"] |
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def search_newsapi(self, query): |
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"""Ricerca su NewsAPI (richiede API key)""" |
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try: |
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if not hasattr(self, 'newsapi_key'): |
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return ["⚠️ NewsAPI non configurato. Imposta la chiave API."] |
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url = f"https://newsapi.org/v2/everything?q={query}&apiKey={self.newsapi_key}" |
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response = requests.get(url, timeout=10) |
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data = response.json() |
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results = [] |
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if 'articles' in data: |
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for article in data['articles'][:3]: |
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title = article['title'] |
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description = article['description'] |
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url = article['url'] |
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results.append(f"📰 NewsAPI:\n{title}\n{description}\nLink: {url}\n") |
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return results |
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except Exception as e: |
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return [f"⚠️ Errore NewsAPI: {str(e)}"] |
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def search_google_scholar(self, query): |
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"""Ricerca su Google Scholar (richiede API)""" |
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try: |
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if not hasattr(self, 'scholar_cx') or not hasattr(self, 'scholar_key'): |
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return ["⚠️ Google Scholar non configurato. Imposta cx e chiave API."] |
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url = f"https://www.googleapis.com/customsearch/v1?key={self.scholar_key}&cx={self.scholar_cx}&q={query}" |
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response = requests.get(url, timeout=10) |
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data = response.json() |
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results = [] |
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if 'items' in data: |
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for item in data['items'][:3]: |
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title = item['title'] |
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snippet = item['snippet'] |
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link = item['link'] |
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results.append(f"📚 Google Scholar:\n{title}\n{snippet}\nLink: {link}\n") |
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return results |
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except Exception as e: |
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return [f"⚠️ Errore Google Scholar: {str(e)}"] |
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def generate_response(self, query): |
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"""Genera una risposta basata sull'intento""" |
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intent = self.analyze_intent(query) |
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data = self.collect_information(query, intent) |
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if not data: |
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return "Non sono riuscito a trovare informazioni rilevanti." |
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if intent['primary'] == 'comparison': |
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return self.process_comparison(data) |
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elif intent['primary'] == 'how_to': |
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return self.process_how_to(data) |
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elif intent['primary'] == 'opinion': |
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return self.process_opinion(data) |
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else: |
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return self.summarize_data(data) |
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def process_comparison(self, data): |
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"""Processa dati per confronti""" |
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comparisons = [] |
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for item in data: |
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if 'vs' in item or 'confronto' in item.lower(): |
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comparisons.append(item) |
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if not comparisons: |
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return "Non ho trovato informazioni dirette per confrontare questi elementi." |
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return "Ecco i principali punti di confronto:\n\n" + "\n\n".join(comparisons[:3]) |
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def process_how_to(self, data): |
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"""Processa dati per procedure""" |
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procedures = [] |
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for item in data: |
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if any(step_word in item.lower() for step_word in ['passo', 'step', 'procedura']): |
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procedures.append(item) |
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if not procedures: |
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return "Non ho trovato istruzioni dettagliate. Prova a cercare con parole chiave come 'guida', 'tutorial' o 'istruzioni'." |
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return "Ecco i passaggi principali:\n\n" + "\n\n".join(procedures[:3]) |
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def process_opinion(self, data): |
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"""Sintetizza opinioni da diverse fonti""" |
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opinions = [] |
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for item in data: |
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if any(opinion_word in item.lower() for opinion_word in ['opinione', 'pensiero', 'considerazione']): |
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opinions.append(item) |
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if not opinions: |
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return "Non ho trovato opinioni esplicite. Posso fornirti informazioni oggettive sulle fonti consultate." |
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return "Ecco alcune opinioni rilevate:\n\n" + "\n\n".join(opinions[:3]) |
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def summarize_data(self, data): |
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"""Sommazzina i dati raccolti""" |
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if not data: |
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return "Non sono riuscito a trovare informazioni rilevanti per la tua query." |
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combined_text = "\n\n---\n\n".join(data) |
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if len(combined_text) > 300: |
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summary = summarizer(combined_text, max_length=500, min_length=100)[0]['summary_text'] |
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return summary |
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else: |
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return combined_text |
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def create_app(): |
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app = ConversationalUBRA() |
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def respond(message, history): |
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response = app.generate_response(message) |
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return "", history + [[message, response]] |
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iface = gr.ChatInterface( |
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fn=respond, |
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examples=[ |
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"Spiega i benefici dell'intelligenza artificiale", |
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"Confronta le energie rinnovabili vs fossili", |
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"Come preparare un piano di business?", |
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"Definisci la sostenibilità aziendale" |
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], |
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title="UBRA - Assistente Conversazionale Intelligente", |
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description="Un AI che ricerca e sintetizza informazioni da fonti affidabili. Chiedi anything!" |
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) |
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return iface |
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if __name__ == "__main__": |
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app = create_app() |
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app.launch() |
