Update app.py

app.py

@@ -10,29 +10,53 @@ from Gradio_UI import GradioUI
 # Below is an example of a tool that does nothing. Amaze us with your creativity!
 
 @tool
-def generate_keywords(topic: str, max_keywords: int) -> str:
-    """A tool that generates the most relevant keywords based on a given topic.
+def generate_keywords(topic: str, num_keywords: int = 5) -> str:
+    """Generates relevant keywords based on the given topic
     Args:
-        topic: the subject or theme for which keywords need to be generated
-        max_keywords: the maximum number of keywords to generate
+        topic: The main topic or subject to generate keywords for
+        num_keywords: Number of keywords to generate (default: 5)
     """
-    # Basic preprocessing to remove common stopwords (without external libraries)
-    stopwords = set(["и", "в", "на", "с", "по", "для", "как", "что", "это", "а", "но", "я", "у", "за", "из", "о", "об", "от"])
+    # Basic word transformations and common patterns
+    prefixes = ['best', 'top', 'how to', 'why', 'what is']
+    suffixes = ['guide', 'tutorial', 'examples', 'tips', 'basics']
+    related_terms = {
+        'programming': ['coding', 'development', 'software', 'algorithms', 'debugging'],
+        'business': ['marketing', 'strategy', 'management', 'startup', 'planning'],
+        'technology': ['tech', 'innovation', 'digital', 'software', 'hardware'],
+        'education': ['learning', 'teaching', 'study', 'course', 'training'],
+        'health': ['wellness', 'fitness', 'nutrition', 'medical', 'healthcare']
+    }
     
-    # Splitting the topic into words and filtering out short words and stopwords
-    words = topic.lower().split()
-    filtered_words = [word for word in words if len(word) > 2 and word not in stopwords]
+    # Clean and normalize the input topic
+    topic = topic.lower().strip()
     
-    # Generating unique keywords by frequency and limiting to max_keywords
-    keyword_frequency = {}
-    for word in filtered_words:
-        keyword_frequency[word] = keyword_frequency.get(word, 0) + 1
+    keywords = set()
     
-    # Sorting keywords by frequency (descending) and selecting top ones
-    sorted_keywords = sorted(keyword_frequency.keys(), key=lambda x: keyword_frequency[x], reverse=True)
-    top_keywords = sorted_keywords[:max_keywords]
+    # Add the original topic
+    keywords.add(topic)
     
-    return ", ".join(top_keywords) if top_keywords else "Нет релевантных ключевых слов"
+    # Add prefix + topic combinations
+    for prefix in prefixes:
+        keywords.add(f"{prefix} {topic}")
+    
+    # Add topic + suffix combinations
+    for suffix in suffixes:
+        keywords.add(f"{topic} {suffix}")
+    
+    # Add related terms if the topic matches any category
+    for category, terms in related_terms.items():
+        if topic in terms or category in topic:
+            keywords.update(terms)
+    
+    # Select the most relevant keywords
+    final_keywords = list(keywords)[:num_keywords]
+    
+    # Format the output
+    return "\n".join([
+        "Generated keywords:",
+        "----------------",
+        *[f"- {keyword}" for keyword in final_keywords]
+    ])
 
 
 @tool