Update llm_app_enhanced.py

llm_app_enhanced.py

@@ -70,42 +70,81 @@ class CodePracticeAssistant:
         # Check if we have curriculum content or need to use generic approach
         if "No specific curriculum content found" in curriculum_content or "could not be retrieved" in curriculum_content:
             # Use generic prompt without curriculum context
-            prompt = f"""Create a programming practice problem for a student learning {topic}.
+            # Add randomization for diversity while keeping it simple
+            difficulty_levels = ["beginner", "intermediate", "challenging"]
+            problem_styles = [
+                "simple calculation", "basic input/output", "pattern printing", 
+                "number games", "text processing", "list operations", "basic functions",
+                "simple loops", "conditional statements", "variable manipulation"
+            ]
+            
+            problem_contexts = [
+                "school project", "homework assignment", "classroom exercise", "practice problem",
+                "simple game", "basic calculator", "data collection", "text processing",
+                "number games", "pattern drawing", "simple simulation", "basic tools"
+            ]
+            
+            selected_difficulty = random.choice(difficulty_levels)
+            selected_style = random.choice(problem_styles)
+            selected_context = random.choice(problem_contexts)
+            
+            prompt = f"""Create a SIMPLE and CLEAR programming practice problem for a student learning {topic}.
 
 Problem Type: {problem_types.get(internal_type, internal_type)}
+Difficulty: {selected_difficulty}
+Style: {selected_style}
+Context: {selected_context}
 
 Requirements:
-- Make it appropriate for beginners to intermediate level
-- Include clear instructions that guide the student
-- Provide a specific, focused problem that requires thinking
-- If it's a debug problem, include the buggy code
-- If it's an optimization problem, provide the original code
-- Make it engaging and educational
-- DO NOT give away the solution or complete answer in the problem statement
-- Focus on what they need to accomplish, not how to do it
+- Make it appropriate for {selected_difficulty} level
+- Use a {selected_style} approach in a {selected_context} setting
+- Keep instructions VERY SIMPLE and step-by-step
+- Focus on ONE main concept at a time
+- Make it engaging but not overwhelming
+- ALWAYS include starter code that students can build upon
+- Use simple, clear language suitable for beginners
+- Avoid complex storylines or too many requirements
+- Make it feel achievable and fun
 
 Format your response as:
-PROBLEM: [The problem description and requirements]
-CODE: [Any starter code if applicable, or "Write your code here:"]
-
-IMPORTANT: If you include example outputs, format them as code blocks like this:
-```
-Example Output:
-1
-1 2
-1 2 3
-1 2 3 4
-1 2 3 4 5
-```
-
-Keep it concise but clear."""
+PROBLEM: [Simple, clear problem description with step-by-step instructions]
+CODE: [Simple starter code (3-8 lines) that students can easily understand and modify]
+
+IMPORTANT: 
+- Keep the problem structure VERY SIMPLE
+- Use clear, direct language
+- Focus on the core concept being learned
+- Make it feel achievable for beginners
+- The starter code should be easy to understand and modify
+
+Create a problem that's engaging but simple enough for students just learning Python!"""
         else:
-            # Use curriculum-based prompt
-            prompt = f"""Create a programming practice problem for a student learning {topic}.
+            # Use curriculum-based prompt with diversity
+            # Add randomization for diversity while keeping it simple
+            difficulty_levels = ["beginner", "intermediate", "challenging"]
+            problem_styles = [
+                "simple calculation", "basic input/output", "pattern printing", 
+                "number games", "text processing", "list operations", "basic functions",
+                "simple loops", "conditional statements", "variable manipulation"
+            ]
+            
+            problem_contexts = [
+                "school project", "homework assignment", "classroom exercise", "practice problem",
+                "simple game", "basic calculator", "data collection", "text processing"
+            ]
+            
+            selected_difficulty = random.choice(difficulty_levels)
+            selected_style = random.choice(problem_styles)
+            selected_context = random.choice(problem_contexts)
+            
+            prompt = f"""Create a SIMPLE and CLEAR programming practice problem for a student learning {topic}.
 
 Curriculum Context: {curriculum_content}
 
 Problem Type: {problem_types.get(internal_type, internal_type)}
+Difficulty: {selected_difficulty}
+Style: {selected_style}
+Context: {selected_context}
 
 Requirements:
 - Base the problem on the curriculum content provided above
@@ -114,34 +153,33 @@ Requirements:
 - Make it appropriate for the skill level shown in the curriculum
 - Include clear instructions that reference the curriculum concepts
 - Provide a specific, focused problem that reinforces what they learned
-- If it's a debug problem, include the buggy code
-- If it's an optimization problem, provide the original code
-- Make it engaging and educational
-- DO NOT give away the solution or complete answer in the problem statement
-- Focus on what they need to accomplish, not how to do it
-- Guide them to think through the problem themselves
+- Use a {selected_style} approach in a {selected_context} setting
+- ALWAYS include starter code that students can build upon
+- Keep instructions VERY SIMPLE and step-by-step
+- Focus on ONE main concept at a time
+- Use simple, clear language suitable for beginners
+- Avoid complex storylines or too many requirements
+- Make it feel achievable and fun
 
 Format your response as:
-PROBLEM: [The problem description and requirements]
-CODE: [Any starter code if applicable, or "Write your code here:"]
+PROBLEM: [Simple, clear problem description with step-by-step instructions]
+CODE: [Simple starter code (3-8 lines) that students can easily understand and modify]
 
-IMPORTANT: If you include example outputs, format them as code blocks like this:
-```
-Example Output:
-1
-1 2
-1 2 3
-1 2 3 4
-1 2 3 4 5
-```
+IMPORTANT: 
+- Keep the problem structure VERY SIMPLE
+- Use clear, direct language
+- Focus on the core concept being learned
+- Make it feel achievable for beginners
+- The starter code should be easy to understand and modify
+- Align with curriculum concepts while keeping it simple
 
-Keep it concise but clear and aligned with the curriculum difficulty."""
+Create a problem that's engaging but simple enough for students just learning Python!"""
 
         try:
             response = self.anthropic_client.messages.create(
                 model="claude-3-5-haiku-20241022",
                 max_tokens=1000,
-                temperature=0.7,
+                temperature=0.3,  # Lower temperature for more structured, predictable problems
                 messages=[{"role": "user", "content": prompt}]
             )
             
@@ -185,7 +223,7 @@ Keep it concise but clear and aligned with the curriculum difficulty."""
             return f"Curriculum content for '{topic}' could not be retrieved."
 
     def analyze_student_code(self, topic, problem_type, problem_description, student_code):
-        """Analyze student's code and provide step-by-step tutoring feedback"""
+        """Analyze student's code and provide feedback based on curriculum"""
         if not self.anthropic_client:
             return "LLM not available. Please check your API key."
         
@@ -213,7 +251,7 @@ Keep it concise but clear and aligned with the curriculum difficulty."""
         
         # Check if we have curriculum content or need to use generic approach
         if "No specific curriculum content found" in curriculum_content or "could not be retrieved" in curriculum_content:
-            # Use generic analysis prompt
+            # Use generic analysis prompt - Tutor style
             prompt = f"""You are a helpful programming tutor. Analyze this student's code and provide step-by-step guidance.
 
 Problem Type: {problem_type}
@@ -232,7 +270,7 @@ Provide a CONCISE, step-by-step analysis:
 
 IMPORTANT: Guide them to figure out the solution themselves. Don't provide complete working code unless they're completely stuck. Focus on hints and direction."""
         else:
-            # Use curriculum-based analysis prompt
+            # Use curriculum-based analysis prompt - Tutor style
             prompt = f"""You are a helpful programming tutor. Analyze this student's code and provide step-by-step guidance.
 
 Curriculum Context: {curriculum_content}
@@ -247,16 +285,16 @@ Analysis Type: {analysis_types.get(internal_type, "General analysis")}
 
 Provide a CONCISE, step-by-step analysis:
 1. **What's Working** (1-2 sentences)
-2. **Areas to Improve** (2-3 specific points based on curriculum)
+2. **Areas to Improve** (2-3 specific points)
 3. **Step-by-Step Solution** (guide them through the process, don't give complete code)
-4. **Key Takeaway** (1 sentence about the concept)
+4. **Key Takeaway** (1 sentence)
 
 IMPORTANT: Guide them to figure out the solution themselves. Don't provide complete working code unless they're completely stuck. Focus on hints and direction based on curriculum concepts."""
 
         try:
             response = self.anthropic_client.messages.create(
                 model="claude-3-5-haiku-20241022",
-                max_tokens=800,
+                max_tokens=1500,
                 temperature=0.7,
                 messages=[{"role": "user", "content": prompt}]
             )
@@ -385,18 +423,32 @@ Thanks! Slide number:"""
             )
             
             # Create answer generation prompt
-            answer_template = """You are a helpful programming tutor. The student asked:
+            answer_template = """Hey there! I'm helping a student understand a programming concept. They asked:
 "{question}"
 Here's what the curriculum slide says about it:
 {slide_content}
-
-Provide a CONCISE, friendly explanation (2-3 sentences max):
-- Answer their specific question
-- Use simple, clear language
-- Reference the curriculum content
-- End with a helpful tip or suggestion
-
-Keep it brief and encouraging!"""
+Could you help me explain this to them in a friendly, educational way? I'd like you to:
+- Break it down in simple terms with comprehensive explanations
+- Use examples if the slide has them, and add more examples if needed
+- Make it step-by-step and easy to follow
+- Add helpful context and real-world applications
+- Use bullet points or lists to make it clear
+- Make sure your answer directly addresses what they asked
+- ALWAYS include practical code examples with explanations
+- Show multiple variations of the concept
+- Include common mistakes and how to avoid them
+- Provide practice exercises or challenges
+- Be comprehensive, detailed, and educational
+- Make it engaging and interesting to read
+
+Format your response with:
+- Clear headings and sections
+- Code examples in proper code blocks
+- Step-by-step explanations
+- Visual organization with bullet points
+- Practical applications and use cases
+
+Thanks for your help! Here's what I'd tell the student:"""
             
             self.answer_prompt = PromptTemplate(
                 input_variables=["question", "slide_content"],
@@ -525,7 +577,7 @@ Keep it brief and encouraging!"""
                 # Get LLM's answer
                 response = self.anthropic_client.messages.create(
                     model="claude-3-5-haiku-20241022",
-                    max_tokens=800,
+                    max_tokens=2000,
                     temperature=0.7,
                     messages=[{"role": "user", "content": prompt}]
                 )
@@ -606,50 +658,75 @@ def analyze_code(topic, problem_type, problem_description, student_code):
     return terminal_output, analysis
 
 def extract_topic_from_query(query):
-    """Extract topic from chat query for practice"""
-    # Simple topic extraction - could be improved with LLM
-    common_topics = ["for loops", "while loops", "functions", "variables", "arrays", "recursion", "debugging", "lists", "dictionaries", "classes", "objects"]
-    query_lower = query.lower()
-    
-    for topic in common_topics:
-        if topic in query_lower:
-            return topic
-    
-    return "programming basics"  # Default topic
+    """Extract topic from chat query for practice using LLM"""
+    try:
+        # Use LLM for better topic extraction
+        prompt = f"""Extract the main programming topic from this student question. 
+        Return ONLY the topic name, nothing else.
+
+        Question: "{query}"
+
+        Examples:
+        - "What are for loops?" → "for loops"
+        - "How do variables work in Python?" → "variables"
+        - "Can you explain functions?" → "functions"
+        - "What's the difference between lists and arrays?" → "lists and arrays"
+        - "How do I debug my code?" → "debugging"
+
+        Topic:"""
+
+        response = practice_assistant.anthropic_client.messages.create(
+            model="claude-3-5-haiku-20241022",
+            max_tokens=100,
+            temperature=0.1,
+            messages=[{"role": "user", "content": prompt}]
+        )
+        
+        topic = response.content[0].text.strip()
+        return topic if topic else "programming basics"
+        
+    except Exception as e:
+        print(f"Error in LLM topic extraction: {e}")
+        # Fallback to simple extraction
+        common_topics = ["for loops", "while loops", "functions", "variables", "arrays", "recursion", "debugging", "lists", "dictionaries", "classes", "objects", "strings", "file handling", "error handling", "modules", "packages"]
+        query_lower = query.lower()
+        
+        for topic in common_topics:
+            if topic in query_lower:
+                return topic
+        
+        return "programming basics"  # Default topic
 
-def go_to_practice(query):
-    """Generate practice problem and return topic for navigation"""
+def generate_practice_for_topic(query):
+    """Generate practice problem and return with navigation message"""
     topic = extract_topic_from_query(query)
-    # Generate a problem for this topic
     problem, code = practice_assistant.generate_practice_problem(topic, "Create Practice Problems")
-    return topic, problem, code
-
-# Custom CSS for better accessibility and styling
-custom_css = """
-.gradio-container {
-    font-size: 16px !important;
-}
-.markdown-text {
-    font-size: 18px !important;
-    line-height: 1.6 !important;
-}
-.problem-description {
-    font-size: 18px !important;
-    font-weight: bold !important;
-}
-.requirements {
-    font-size: 16px !important;
-    color: #666 !important;
-}
-.terminal-output {
-    font-family: 'Courier New', monospace !important;
-    font-size: 14px !important;
-    background-color: #f5f5f5 !important;
-}
+    
+    # Create a message to guide user to practice tab
+    navigation_message = f"""
+🎯 **Practice Problem Generated!**
+
+I've created a practice problem for **{topic}** and loaded it into the Code Practice tab.
+
+**To continue practicing:**
+1. Click on the **"💻 Code Practice"** tab above
+2. You'll see the topic and problem already filled in
+3. Write your solution and click "Analyze My Code"
+
+**Topic:** {topic}
+**Problem Type:** Create Practice Problems
+
+Ready to practice? Switch to the Code Practice tab! 🚀
 """
+    
+    return navigation_message, topic, problem, code
+
+
+
+with gr.Blocks(title="LLM Curriculum Assistant", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🤖 LLM Curriculum Assistant\nYour AI programming tutor with LLM-powered content selection and code practice!")
+    
 
-with gr.Blocks(title="Enhanced LLM Curriculum Assistant", theme=gr.themes.Soft(), css=custom_css) as demo:
-    gr.Markdown("# 🤖 Enhanced LLM Curriculum Assistant\nYour AI programming tutor with improved UX and accessibility!")
     
     with gr.Tabs() as tabs:
         # Tab 1: Chat Assistant
@@ -666,7 +743,7 @@ with gr.Blocks(title="Enhanced LLM Curriculum Assistant", theme=gr.themes.Soft()
                         lines=3
                     )
                     submit = gr.Button("🤖 Ask AI", variant="primary", size="lg")
-                    answer = gr.Markdown(label="LLM Generated Answer", elem_classes=["markdown-text"])
+                    answer = gr.Markdown(label="LLM Generated Answer")
                     
                     # Practice button
                     practice_btn = gr.Button("💻 Practice This Topic", variant="secondary", size="lg", visible=False)
@@ -725,7 +802,7 @@ with gr.Blocks(title="Enhanced LLM Curriculum Assistant", theme=gr.themes.Soft()
                     generate_btn = gr.Button("🎲 Generate Problem", variant="primary", size="lg")
                     
                     gr.Markdown("#### 📋 Problem Description")
-                    problem_description = gr.Markdown(label="Problem will appear here...", elem_classes=["problem-description"])
+                    problem_description = gr.Markdown(label="Problem will appear here...")
                     
                     gr.Markdown("#### 💻 Starter Code (if applicable)")
                     starter_code = gr.Code(
@@ -753,12 +830,11 @@ with gr.Blocks(title="Enhanced LLM Curriculum Assistant", theme=gr.themes.Soft()
                         label="Terminal Output",
                         lines=8,
                         value="# Code execution output will appear here...",
-                        interactive=False,
-                        elem_classes=["terminal-output"]
+                        interactive=False
                     )
                     
                     gr.Markdown("#### 📊 AI Analysis")
-                    analysis_output = gr.Markdown(label="Analysis will appear here...", elem_classes=["markdown-text"])
+                    analysis_output = gr.Markdown(label="Analysis will appear here...")
             
             # Event handlers for practice
             generate_btn.click(
@@ -773,25 +849,12 @@ with gr.Blocks(title="Enhanced LLM Curriculum Assistant", theme=gr.themes.Soft()
                 outputs=[terminal_output, analysis_output]
             )
             
-            # Practice button from chat - populate topic and generate problem
-            def practice_with_navigation(query):
-                topic, problem, code = go_to_practice(query)
-                return topic, problem, code
-            
+            # Practice button - generate problem and show navigation message
             practice_btn.click(
-                fn=practice_with_navigation,
+                fn=generate_practice_for_topic,
                 inputs=[question],
-                outputs=[topic_input, problem_description, starter_code]
-            )
-            
-            # Add a separate event to switch tabs
-            def switch_to_practice():
-                return gr.update(selected=1)
-            
-            practice_btn.click(
-                fn=switch_to_practice,
-                outputs=[tabs]
+                outputs=[answer, topic_input, problem_description, starter_code]
             )
 
 if __name__ == "__main__":
-    demo.launch() 
+    demo.launch()