Update app.py

Added a user pref tool , to determine whether the results should be based on datasets or web search

app.py

@@ -21,14 +21,11 @@ def calculate_age(birthdate: str) -> int:
         The calculated age as an integer, or -1 if parsing fails.
     """
     try:
-        birth_date = parser.parse(birthdate, fuzzy=True)  # Auto-detects date format
+        birth_date = parser.parse(birthdate, fuzzy=True)
         today = datetime.today()
-
-        # Ensure birthdate is not in the future
         if birth_date > today:
-            return -1  # Invalid: Future birthdate
+            return -1
         
-        # Calculate age
         age = today.year - birth_date.year - ((today.month, today.day) < (birth_date.month, birth_date.day))
         return age
 
@@ -54,7 +51,7 @@ final_answer = FinalAnswerTool()
 search_tool = DuckDuckGoSearchTool()
 
 @tool
-def get_lifestyle_recommendations(age: int) -> str:
+def get_lifestyle_recommendations_web(age: int) -> str:
     """Fetches lifestyle recommendations based on age using web search.
     Args:
         age: The calculated age of the user.
@@ -66,7 +63,7 @@ def get_lifestyle_recommendations(age: int) -> str:
 
 @tool
 def get_lifestyle_recommendations_offline(age: int) -> str:
-    """Provides daily lifestyle habits based on age.
+    """Provides daily lifestyle habits based on age with the following dataset.
     Args:
         age: The age of the user.
     """
@@ -85,7 +82,41 @@ def get_lifestyle_recommendations_offline(age: int) -> str:
     
     return "Age not in expected range. Try again."
 
+@tool
+def determine_user_preference(user_input: str) -> bool:
+    """
+    Uses an LLM to classify whether the user prefers real-time (web-based) or offline recommendations.
+
+    Args:
+        user_input: The user's input as natural language.
+
+    Returns:
+        True if web-based recommendations should be used, False otherwise.
+    """
+    prompt = f"""
+    The user has provided the following request: "{user_input}".
+    Determine if the user is asking for real-time, AI-driven, or external data-based insights. 
+    Respond with only 'yes' (for web-based recommendations) or 'no' (for offline recommendations).
+    """
+    response = agent.run(prompt) 
+    return response.strip().lower() == "yes"
+    
 
+@tool
+def get_lifestyle_recommendations(age: int) -> str:
+    """Fetches lifestyle recommendations based on age.
+    Args:
+        age: The calculated age of the user.
+    Returns:
+        A summary of lifestyle recommendations.
+    """
+     if determine_user_preference(user_input): 
+        recommendations = get_lifestyle_recommendations_web(age)  #web search
+    else:
+        recommendations = get_lifestyle_recommendations_offline(age)  # Offline Dataset
+    
+    return recommendations
+    
 # If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
 # model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' 
 
@@ -105,7 +136,7 @@ with open("prompts.yaml", 'r') as stream:
     
 agent = CodeAgent(
     model=model,
-    tools=[final_answer,get_lifestyle_recommendations,calculate_age,get_lifestyle_recommendations_offline], ## add your tools here (don't remove final answer)
+    tools=[final_answer,get_lifestyle_recommendations,calculate_age], ## add your tools here (don't remove final answer)
     max_steps=6,
     verbosity_level=1,
     grammar=None,