Update app.py

app.py

@@ -123,6 +123,31 @@ You recognize that students may seek direct answers to homework, assignments, or
 - **Encourage original thinking**: Help students develop their own reasoning and analytical skills
 - **Suggest study strategies**: Recommend effective learning approaches for the subject matter
 
+# Visual Learning Enhancement
+You have the ability to create graphs and charts to enhance your explanations. Use this capability proactively when:
+
+- Explaining mathematical concepts (functions, distributions, relationships)
+- Teaching statistical analysis or data interpretation
+- Discussing scientific trends, patterns, or experimental results
+- Comparing different options, outcomes, or scenarios
+- Illustrating economic principles, business metrics, or financial concepts
+- Showing survey results, demographic data, or research findings
+- Demonstrating any concept where visualization aids comprehension
+
+**When to create graphs:**
+- The concept involves numerical data or relationships
+- Visual representation would clarify a complex idea
+- Students benefit from seeing patterns or comparisons
+- You're teaching about graph interpretation itself
+- The topic involves trends, distributions, or proportions
+- A multiple choice question requires a visual, such as a graph, to be answered.
+
+**How to create graphs:**
+Generate realistic, educational data that illustrates your teaching point. Create meaningful examples that help students understand the underlying concepts, not just random numbers.
+
+Example: When explaining normal distribution, create a graph showing test scores distributed normally around a mean, with appropriate labels and educational context.
+
+
 ## Tool Usage
 You have access to a create_graph tool. Use this tool naturally when a visual representation would enhance understanding or when discussing concepts that involve data, relationships, patterns, or quantitative information. Consider creating graphs for:
 - Mathematical concepts (functions, distributions, relationships)
@@ -381,28 +406,75 @@ class Qwen25SmallLLM(LLM):
         return "qwen25_small"
     
 def create_langchain_agent():
+    """Creates the LangChain agent with educational graph capabilities."""
+    logger.info("Creating educational LangChain agent...")
+    
     # Use the smaller local model
     llm = Qwen25SmallLLM()
     
-    tools = [CreateGraphTool()]
+    # Use the educational graph tool
+    tools = [create_educational_graph_tool()]
     memory = ConversationBufferWindowMemory(
         memory_key="chat_history",
         k=10,
         return_messages=True
     )
     
+    # Use standard agent type
     agent = initialize_agent(
         tools=tools,
         llm=llm,
-        agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
         memory=memory,
-        verbose=False,
+        verbose=False,  # Set to True for debugging
         max_iterations=3,
         early_stopping_method="generate"
     )
     
+    logger.info("Educational LangChain agent created successfully!")
     return agent
 
+# Example of how the AI should use the tool
+def example_usage_for_ai():
+    """
+    This shows how the AI should autonomously create graphs in its responses.
+    The AI doesn't wait for user data - it creates meaningful educational examples.
+    """
+    
+    # Example: Teaching about normal distribution
+    example_config = {
+        "data": {
+            "Below 60": 5,
+            "60-70": 15, 
+            "70-80": 25,
+            "80-90": 35,
+            "90-100": 20
+        },
+        "plot_type": "bar",
+        "title": "Typical Test Score Distribution",
+        "x_label": "Score Range",
+        "y_label": "Number of Students",
+        "educational_context": "This shows how test scores often follow a bell-curve pattern, with most students scoring in the middle range."
+    }
+    
+    # Example: Teaching about compound interest
+    compound_interest_example = {
+        "data": {
+            "Year 1": 1000,
+            "Year 5": 1276,
+            "Year 10": 1629,
+            "Year 15": 2079,
+            "Year 20": 2653
+        },
+        "plot_type": "line", 
+        "title": "Compound Interest Growth ($1000 at 5% Annual)",
+        "x_label": "Time (Years)",
+        "y_label": "Account Value ($)",
+        "educational_context": "Notice how the growth accelerates over time - this is the power of compound interest!"
+    }
+    
+    return "These examples show how the AI creates educational data to illustrate concepts"
+
 # --- Global Agent Instance ---
 agent = None