Update app.py

app.py

@@ -240,28 +240,45 @@ LEARNING_RESOURCES = {
     },
     "Video Tutorials": {
         "python_beginner": [
+            {"title": "Python Full Course for Beginners", "url": "https://www.youtube.com/watch?v=_uQrJ0TkZlc"},
             {"title": "CS50's Introduction to Programming with Python", "url": "https://cs50.harvard.edu/python/"},
+            {"title": "Python Tutorial - Python for Beginners", "url": "https://www.youtube.com/watch?v=_uQrJ0TkZlc"},
             {"title": "freeCodeCamp Python Course", "url": "https://www.freecodecamp.org/learn/scientific-computing-with-python/"}
         ],
         "python_intermediate": [
+            {"title": "Corey Schafer Python Tutorials", "url": "https://www.youtube.com/user/schafer5"},
+            {"title": "Advanced Python Features", "url": "https://www.youtube.com/playlist?list=PLP8GkvaIxJP0VAXF3USi9U4JnpxnHjT_"},
+            {"title": "Python OOP Tutorials", "url": "https://www.youtube.com/playlist?list=PLzMcBGfZo4-l1MqB1zoYfqzlj_HH-ZzXt"},
             {"title": "MIT OpenCourseWare: Python", "url": "https://ocw.mit.edu/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/"}
         ],
         "data_science_beginner": [
+            {"title": "Python for Data Science Course", "url": "https://www.youtube.com/watch?v=LHBE6Q9XlzI"},
+            {"title": "Data Analysis with Python - Full Course", "url": "https://www.youtube.com/watch?v=r-uOLxNrNk8"},
+            {"title": "Statistics Fundamentals", "url": "https://www.youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9"},
             {"title": "freeCodeCamp Data Analysis Course", "url": "https://www.freecodecamp.org/learn/data-analysis-with-python/"}
         ],
         "data_science_advanced": [
+            {"title": "StatQuest: Machine Learning", "url": "https://www.youtube.com/playlist?list=PLblh5JKOoLUIcdlgu78MnlATeyx4cEVeR"},
             {"title": "Machine Learning Course by Andrew Ng", "url": "https://www.coursera.org/learn/machine-learning"},
-            {"title": "Deep Learning Specialization", "url": "https://www.deeplearning.ai/deep-learning-specialization/"}
+            {"title": "Deep Learning Specialization", "url": "https://www.deeplearning.ai/deep-learning-specialization/"},
+            {"title": "Data Science Full Course", "url": "https://www.youtube.com/watch?v=ua-CiDNNj30"}
         ],
         "ai_specialization": [
-            {"title": "MIT 6.S191: Introduction to Deep Learning", "url": "http://introtodeeplearning.com/"}
+            {"title": "Stanford CS231n: CNN for Visual Recognition", "url": "https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv"},
+            {"title": "Deep Learning Lectures by Lex Fridman", "url": "https://www.youtube.com/playlist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf"},
+            {"title": "MIT 6.S191: Introduction to Deep Learning", "url": "http://introtodeeplearning.com/"},
+            {"title": "Stanford CS224N: NLP with Deep Learning", "url": "https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z"}
         ],
         "generative_ai": [
             {"title": "Neural Networks: Zero to Hero", "url": "https://karpathy.ai/zero-to-hero.html"},
+            {"title": "LLM Bootcamp", "url": "https://www.youtube.com/watch?v=twHxmU9OxDU"},
+            {"title": "Diffusion Models Explained", "url": "https://www.youtube.com/watch?v=fbLgFrlTnGU"},
             {"title": "Prompt Engineering for LLMs", "url": "https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/"}
         ],
         "agentic_ai": [
             {"title": "Building AI Agents with LangChain", "url": "https://www.youtube.com/watch?v=iw2Wcw7qPuE"},
+            {"title": "LLM Agents Tutorial", "url": "https://www.youtube.com/watch?v=RUzgloRlHIc"},
+            {"title": "Reinforcement Learning Course", "url": "https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ"},
             {"title": "AutoGPT and Multi-Agent Systems", "url": "https://www.youtube.com/watch?v=4YaILFaUXTo"}
         ]
     },
@@ -907,9 +924,9 @@ def format_learning_paths(paths):
     
     result = "### Recommended Learning Paths\n\n"
     for i, path in enumerate(paths, 1):
-        result += f"**{i}. {path['title']}**\n"
+        result += f"{i}. {path['title']}\n"
         result += f"{path['description']}\n\n"
-        result += "**Modules:**\n"
+        result += "*Modules:*\n"
         for module in path['modules']:
             result += f"- {module}\n"
         result += "\n"
@@ -1004,7 +1021,7 @@ def generate_recommendations(session_id):
     
     # Build the recommendation markdown
     markdown = f"## Your Personalized Learning Path: {learning_path['title']}\n\n"
-    markdown += f"*{learning_path['description']}*\n\n"
+    markdown += f"{learning_path['description']}\n\n"
     
     markdown += "### Learning Modules\n"
     for i, module in enumerate(learning_path['modules'], 1):
@@ -1100,11 +1117,11 @@ def add_generative_ai_info():
     
     ### Key Concepts in Generative AI:
     
-    - **Large Language Models (LLMs)**: Text generation systems like GPT-4, LLaMA, Claude, etc.
-    - **Diffusion Models**: For image generation (DALL-E, Midjourney, Stable Diffusion)
-    - **Prompt Engineering**: The art of crafting inputs to get desired outputs
-    - **Fine-tuning**: Adapting pre-trained models for specific domains or tasks
-    - **RLHF (Reinforcement Learning from Human Feedback)**: Method for aligning AI with human preferences
+    - *Large Language Models (LLMs)*: Text generation systems like GPT-4, LLaMA, Claude, etc.
+    - *Diffusion Models*: For image generation (DALL-E, Midjourney, Stable Diffusion)
+    - *Prompt Engineering*: The art of crafting inputs to get desired outputs
+    - *Fine-tuning*: Adapting pre-trained models for specific domains or tasks
+    - *RLHF (Reinforcement Learning from Human Feedback)*: Method for aligning AI with human preferences
     
     Learning generative AI involves understanding these foundation models, how they work, and how to effectively use and customize them for various applications.
     """
@@ -1118,11 +1135,11 @@ def add_agentic_ai_info():
     
     ### Key Concepts in Agentic AI:
     
-    - **Planning & Decision Making**: AI systems that can formulate and execute plans
-    - **Tool Use**: AI that can leverage external tools and APIs
-    - **Autonomous Execution**: Systems that can work without constant human supervision
-    - **Multi-agent Systems**: Multiple AI agents collaborating or competing
-    - **Memory & Context Management**: How agents maintain state across interactions
+    - *Planning & Decision Making*: AI systems that can formulate and execute plans
+    - *Tool Use*: AI that can leverage external tools and APIs
+    - *Autonomous Execution*: Systems that can work without constant human supervision
+    - *Multi-agent Systems*: Multiple AI agents collaborating or competing
+    - *Memory & Context Management*: How agents maintain state across interactions
     
     Agentic AI represents an evolution from AI as a passive tool to AI as an active collaborator that can work independently while remaining aligned with human goals and values.
     """
@@ -1378,6 +1395,6 @@ def create_chatbot():
     return demo
 
 # Run the chatbot
-if __name__ == "__main__":
+if _name_ == "_main_":
     app = create_chatbot()
     app.launch()