Update app.py

app.py

@@ -1,5 +1,6 @@
 import os
 import sys
+import re
 import gradio as gr
 from langchain_openai import ChatOpenAI
 from langgraph.prebuilt import create_react_agent
@@ -9,6 +10,7 @@ from langchain_mcp_adapters.tools import load_mcp_tools
 from langchain_core.messages import HumanMessage, SystemMessage
 
 # --- Configuration ---
+# Ensure your API key is set in your environment variables
 NEBIUS_API_KEY = os.getenv("NEBIUS_API_KEY") 
 NEBIUS_BASE_URL = "https://api.studio.nebius.ai/v1/"
 
@@ -21,29 +23,41 @@ AVAILABLE_MODELS = [
 # --- System Prompt ---
 SYSTEM_PROMPT = """You are a 'Vibe Coding' Python Tutor. 
 Your goal is to teach by DOING and then providing resources.
+
 BEHAVIOR GUIDELINES:
 1. **Greetings & Small Talk**: If the user says "hello", "hi", or asks non-coding questions, respond conversationally and politely. Ask them what they want to learn today. 
    - DO NOT generate the lesson structure, files, or resources for simple greetings.
+
 2. **Teaching Mode**: ONLY when the user asks a coding question or requests a topic (e.g., "dictionaries", "how do loops work"):
    - **The Lesson**: Explain the concept clearly.
    - **The Code**: ALWAYS create a Python file, run it, and show the output using tools ('write_file', 'run_python_script').
    - **The Context**: Use 'list_directory' to see the student's workspace.
-   CRITICAL: When in "Teaching Mode", you must end your response with these exact separators:
+
+   CRITICAL FORMATTING INSTRUCTIONS:
+   When in "Teaching Mode", you MUST end your response by strictly following this format. 
+   Do not add extra text between the sections.
+
+   (End of your main lesson text)
+   
    ---SECTION: VIDEOS---
    (List 2-3 YouTube search queries or URLs relevant to the topic)
+
    ---SECTION: ARTICLES---
    (List 2-3 documentation links or course names, e.g., RealPython, FreeCodeCamp)
+
    ---SECTION: QUIZ---
    (Create 2 short multiple-choice question. Use HTML <details> and <summary> tags to hide the answer. Example: 
    **Question**: ...
    - A) ...
    - B) ...
-
    <details><summary>👀 Reveal Answers</summary>Correct is A because...</details>)
 """
 
 def parse_agent_response(full_text):
-    """Splits the single LLM response into 4 UI components."""
+    """
+    Robust parsing using Regex to handle LLM formatting inconsistencies.
+    Splits the single LLM response into 4 UI components: Chat, Videos, Articles, Quiz.
+    """
     chat_content = full_text
     
     # Default values for "waiting" state
@@ -51,36 +65,57 @@ def parse_agent_response(full_text):
     articles = "### 📚 Articles & Courses\n*Ask a coding question to get resources!*"
     quiz = "### 🧠 Quick Quiz\n*Ask a coding question to take a quiz!*"
 
+    # Regex patterns that allow for slight variations in spacing or casing
+    # Examples caught: "---SECTION: VIDEOS---", "---SECTION:VIDEOS ---", "--- section: videos ---"
+    video_pattern = r"---SECTION:\s*VIDEOS\s*---"
+    article_pattern = r"---SECTION:\s*ARTICLES\s*---"
+    quiz_pattern = r"---SECTION:\s*QUIZ\s*---"
+
     try:
-        # Only try to split if the separators exist (i.e., we are in Teaching Mode)
-        if "---SECTION: VIDEOS---" in full_text:
-            parts = full_text.split("---SECTION: VIDEOS---")
-            chat_content = parts[0].strip()
-            remainder = parts[1]
+        # 1. Check if we are in Teaching Mode (look for Video section)
+        # re.split returns a list. If found, index 0 is before the split, index 1 is after.
+        split_video = re.split(video_pattern, full_text, flags=re.IGNORECASE, maxsplit=1)
+        
+        if len(split_video) > 1:
+            chat_content = split_video[0].strip()
+            remaining_resources = split_video[1]
+
+            # 2. Extract Videos vs Articles
+            split_article = re.split(article_pattern, remaining_resources, flags=re.IGNORECASE, maxsplit=1)
             
-            if "---SECTION: ARTICLES---" in remainder:
-                v_parts = remainder.split("---SECTION: ARTICLES---")
-                # Add headers back for the UI
-                videos = f"### 📺 Recommended Videos\n{v_parts[0].strip()}"
-                remainder = v_parts[1]
+            if len(split_article) > 0:
+                # Everything before ARTICLE tag is Video content
+                video_content = split_article[0].strip()
+                if video_content:
+                    videos = f"### 📺 Recommended Videos\n{video_content}"
+            
+            if len(split_article) > 1:
+                remaining_quiz = split_article[1]
+
+                # 3. Extract Articles vs Quiz
+                split_quiz = re.split(quiz_pattern, remaining_quiz, flags=re.IGNORECASE, maxsplit=1)
                 
-                if "---SECTION: QUIZ---" in remainder:
-                    a_parts = remainder.split("---SECTION: QUIZ---")
-                    articles = f"### 📚 Articles & Courses\n{a_parts[0].strip()}"
-                    quiz = f"### 🧠 Quick Quiz\n{a_parts[1].strip()}"
-                else:
-                    articles = f"### 📚 Articles & Courses\n{remainder.strip()}"
-            else:
-                videos = f"### 📺 Recommended Videos\n{remainder.strip()}"
+                if len(split_quiz) > 0:
+                    article_content = split_quiz[0].strip()
+                    if article_content:
+                        articles = f"### 📚 Articles & Courses\n{article_content}"
+                
+                if len(split_quiz) > 1:
+                    quiz_content = split_quiz[1].strip()
+                    if quiz_content:
+                        quiz = f"### 🧠 Quick Quiz\n{quiz_content}"
+
     except Exception as e:
         print(f"Parsing error: {e}")
+        # Fallback: Just show everything in chat if parsing fails completely, so no data is lost
+        chat_content = full_text
 
     return chat_content, videos, articles, quiz
 
 async def run_tutor_dashboard(user_message, model_id):
     """
     Main function to run the agent loop.
-    Now accepts 'model_id' to select the LLM dynamically.
+    Accepts 'model_id' to select the LLM dynamically.
     """
     server_params = StdioServerParameters(
         command=sys.executable, 
@@ -103,7 +138,6 @@ async def run_tutor_dashboard(user_message, model_id):
 
             agent_executor = create_react_agent(llm, tools)
             
-            # Prepend the SystemMessage to ensure the agent follows instructions
             inputs = {
                 "messages": [
                     SystemMessage(content=SYSTEM_PROMPT),
@@ -116,7 +150,6 @@ async def run_tutor_dashboard(user_message, model_id):
             return parse_agent_response(final_text)
 
 # --- Gradio Dashboard UI ---
-# Professional "Slate" theme
 theme = gr.themes.Soft(
     primary_hue="slate",
     secondary_hue="indigo",
@@ -133,11 +166,9 @@ theme = gr.themes.Soft(
 with gr.Blocks(title="AI Python Tutor", theme=theme, fill_height=True) as demo:
     # --- Header with Model Selector ---
     with gr.Row(variant="compact", elem_classes="header-row"):
-        # scale=1: Consumes all available space, pushing the next column to the right
         with gr.Column(scale=1):
             gr.Markdown("## 🐍 AI Python Tutor")
         
-        # scale=0: Takes only minimum required width, sitting on the far right
         with gr.Column(scale=0, min_width=250):
             model_selector = gr.Dropdown(
                 choices=AVAILABLE_MODELS,
@@ -151,7 +182,6 @@ with gr.Blocks(title="AI Python Tutor", theme=theme, fill_height=True) as demo:
     with gr.Row(equal_height=True):
         # Left Column: Chat & Input
         with gr.Column(scale=3, variant="panel"):
-            # Custom Header with Focus Mode Button
             with gr.Row():
                 gr.Markdown("### 💬 Interactive Session")
                 fullscreen_btn = gr.Button("⛶ Focus Mode", size="sm", variant="secondary", scale=0, min_width=120)
@@ -191,7 +221,6 @@ with gr.Blocks(title="AI Python Tutor", theme=theme, fill_height=True) as demo:
         with gr.Column(scale=2) as right_col:
             gr.Markdown("### 🎒 Learning Dashboard")
             
-            # Tabbed interface for cleaner look
             with gr.Tabs():
                 with gr.TabItem("📺 Videos"):
                     video_box_side = gr.Markdown(value="### Recommended Videos\n*Ask a topic to see video suggestions!*")
@@ -265,14 +294,12 @@ with gr.Blocks(title="AI Python Tutor", theme=theme, fill_height=True) as demo:
         video_box_bottom, article_box_bottom, quiz_box_bottom
     ]
 
-    # Added model_selector to inputs
     submit_btn.click(
         respond, 
         [msg, chatbot, model_selector], 
         outputs_list
     )
     
-    # Added model_selector to inputs
     msg.submit(
         respond, 
         [msg, chatbot, model_selector],