Refactor get_news function to use web_search for fetching articles and add logging for better traceability

tools.py

@@ -9,8 +9,7 @@ from llms import llm_openai
 
 
 last_news = []
-
-
+    
 def get_news(topics: List[str]) -> List[Dict]:
     """
     Fetches news articles related to the specified topics using the NewsAPI, analyzes their sentiment and named entities, and returns a list of processed news items.
@@ -31,34 +30,22 @@ def get_news(topics: List[str]) -> List[Dict]:
         None. All exceptions are caught and returned as error messages in the result.
     """
     global last_news
-    api_key = os.environ.get("NEWS_API_KEY")
-    base_url = "https://newsapi.org/v2/everything"
-    queries = " OR ".join(topics)
-    params = {
-        "q": queries,
-        "apiKey": api_key,
-        "pageSize": 10,
-    }
-    try:
-        response = requests.get(base_url, params=params)
-        response.raise_for_status()
-        data = response.json()
-        articles = data.get("articles", [])
-        last_news = []
-        for idx, article in enumerate(articles, 1):
-            summary = article.get("description", "No description available")
-            sentiment = analyze_sentiment(summary)
-            entities = recognize_entities(summary)
-            last_news.append({
-                "index": idx,
-                "title": article.get("title", "No title"),
-                "summary": summary,
-                "sentiment": sentiment,
-                "entities": entities
-            })
-        return last_news
-    except requests.RequestException as e:
-        return [{"error": f"Failed to fetch news: {str(e)}"}]
+    print(f"Fetching news for topics: {topics}")
+    
+    last_news.clear()  # Clear previous news to avoid duplication
+    for topic in topics:
+        search_results = web_search(f'Find the latest news related to: {topic}')
+        if search_results:
+            for res in search_results:
+                last_news.append({
+                    "index": len(last_news) + 1,
+                    "title": res.get("title", "No title available"),
+                    "summary": res.get("content", "No summary available"),
+                    # "sentiment": analyze_sentiment(res.get("snippet", "")),
+                    # "entities": recognize_entities(res.get("snippet", ""))
+                })
+    print(f"Found {len(last_news)} articles.")
+    return last_news
 
 
 
@@ -126,6 +113,7 @@ def generate_implications(article_index: int) -> str:
         str: A message containing the implications for the specified article, or an error message if the index is invalid.
     """
     global last_news
+    print(f"Generating implications for article index: {article_index}")
     if not (1 <= article_index <= len(last_news)):
         return "Invalid article index."
     article = last_news[article_index - 1]
@@ -137,6 +125,7 @@ def generate_implications(article_index: int) -> str:
         )
     except Exception as e:
         return f"Error generating implications: {str(e)}"
+    print(f"Generated implications: {result.message.content}")
     return f"Implications for article {article_index}: {result.message.content}"
 
 
@@ -152,6 +141,7 @@ def web_search(query: str) -> List[Dict]:
     """
     client = TavilyClient(os.environ.get("TAVILY_API_KEY"))
     response = client.search(query)
+    print(f"Web search results for query '{query}': {response}")
     return response['results'] if 'results' in response else []
 
 
@@ -187,6 +177,7 @@ def get_lead_up_events(article_index: int) -> str:
         str: A formatted string summarizing the lead-up events or background information for the article's topic.
     """
     global last_news
+    print(f"Getting lead-up events for article index: {article_index}")
     if not (1 <= article_index <= len(last_news)):
         return "Invalid article index."
     article = last_news[article_index - 1]
@@ -211,8 +202,26 @@ def get_lead_up_events(article_index: int) -> str:
         )
     except Exception as e:
         return f"Error generating background information: {str(e)}"
+    print(f"Generated background information: {result.message.content}")
     return f"Background information for article {article_index}: {result.message.content}"
 
+def call_llm(prompt: str) -> str:
+    """
+    Calls the LLM with a given prompt and returns the response.
+
+    Args:
+        prompt: The input prompt to send to the LLM.
+
+    Returns:
+        str: The response from the LLM.
+    """
+    try:
+        result = llm_openai.chat(
+            messages=[ChatMessage(role="user", content=prompt)]
+        )
+        return result.message.content
+    except Exception as e:
+        return f"Error calling LLM: {str(e)}"
 
 def get_social_media_opinions(article_index: int) -> str:
     """
@@ -228,27 +237,24 @@ def get_social_media_opinions(article_index: int) -> str:
         str: A summary string indicating the categorized number of positive and negative opinions about the event.
     """
     global last_news
+    print(f"Getting social media opinions for article index: {article_index}")
     if not (1 <= article_index <= len(last_news)):
         return "Invalid article index."
     article = last_news[article_index - 1]
     title = article["title"]
-    posts = web_search(f'What are the social media reactions to: {title}?')
-    positive_count = 0
-    negative_count = 0
-    for post in posts:
-        sentiment = analyze_sentiment(post["content"])
-        if sentiment == "POSITIVE":
-            positive_count += 1
-        elif sentiment == "NEGATIVE":
-            negative_count += 1
-
-    def categorize(count):
-        if count <= 10:
-            return "low"
-        elif count <= 30:
-            return "medium"
-        else:
-            return "high"
-    positive_level = categorize(positive_count)
-    negative_level = categorize(negative_count)
-    return f"There are a {positive_level} number of positive opinions and a {negative_level} number of negative opinions about this event."
+    
+    pos_posts = web_search(f'What are the positive social media reactions related to: {title}?')
+    neg_posts = web_search(f'What are the negative social media reactions related to: {title}?')
+    
+    # haz un resumen con el llm de los posts positivos
+    pos_summary = call_llm('Make a summary of the following social media posts: ' + str(pos_posts))
+    neg_summary = call_llm('Make a summary of the following social media posts: ' + str(neg_posts))
+
+    print(f"Positive summary: {pos_summary}")
+    print(f"Negative summary: {neg_summary}")
+
+    return f"""
+    Social Media Opinions for Article {article_index}:
+    Positive Summary: {pos_summary}
+    Negative Summary: {neg_summary}
+    """