finish the idea of micro learning part 5

README.md

@@ -7,24 +7,23 @@ sdk: gradio
 sdk_version: 5.29.0
 app_file: app.py
 pinned: false
-short_description: micro learning
+short_description: A microlearning platform demo
 ---
 
+# Micro Learning Platform
 
-/micro_learning_platform
-  /api
-    main.py          # FastAPI/Flask entry point
-    models.py        # Data models
-    routes.py        # API endpoints
-  /ai
-    huggingface.py   # Hugging Face integration
-    agents.py        # Your MCP agents
-  /data
-    content.py       # Content management
-    users.py         # User management
-  /frontend
-    templates/       # Simple templates if needed
-    static/          # CSS/JS assets
-  app.py             # Main application entry
+This is a demonstration of a microlearning platform that uses AI to help users learn concepts in small, digestible chunks.
+
+## Features
+- Browse microlearning content
+- Get summaries of learning modules
+- Ask questions about content
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Project Structure
+```python
+/micro_learning_platform
+  /api              # Future API endpoints
+  /ai               # Future AI integration
+  /data             # Future data management
+  /frontend         # Future web interface
+  app.py            # Main application entry

app.py

@@ -1,31 +1,52 @@
-# Minimal app.py for Hugging Face Spaces
+# Improved app.py with real summarization using Hugging Face model
 import gradio as gr
+from transformers import pipeline
+
+# Initialize the summarization model
+try:
+    # Use a smaller, faster model for summarization
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=-1)  # Using CPU
+    print("Summarization model loaded successfully!")
+except Exception as e:
+    print(f"Error loading summarization model: {e}")
+    summarizer = None
 
 # Mock data for demonstration
 sample_content = {
     "module1": {
         "title": "Introduction to AI",
-        "text": "Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction."
+        "text": "Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction. AI can be categorized as either weak or strong. Weak AI is designed to complete a narrow task, like facial recognition. Strong AI can perform any intellectual task that a human being can do."
     },
     "module2": {
         "title": "Python Basics",
-        "text": "Python is a high-level, interpreted programming language known for its readability and simplicity. It is widely used in data science, machine learning, and web development."
+        "text": "Python is a high-level, interpreted programming language known for its readability and simplicity. It is widely used in data science, machine learning, and web development. Python's syntax allows programmers to express concepts in fewer lines of code than languages like C++ or Java. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming."
     },
     "module3": {
         "title": "Microlearning Concepts",
-        "text": "Microlearning is an educational strategy that focuses on delivering content in small, specific bursts. Each learning unit typically addresses one learning objective and takes 3-5 minutes to complete."
+        "text": "Microlearning is an educational strategy that focuses on delivering content in small, specific bursts. Each learning unit typically addresses one learning objective and takes 3-5 minutes to complete. This approach is particularly effective for modern learners with limited time and attention spans. It often incorporates multimedia elements and is optimized for mobile devices, allowing learning to happen anywhere."
     }
 }
 
-# Mock functions
+# Function to generate real summaries using Hugging Face model
 def get_summary(content_id):
-    """Generate a mock summary for demonstration"""
+    """Generate a summary using a HuggingFace model"""
     if content_id not in sample_content:
         return "Content not found"
     
     text = sample_content[content_id]["text"]
-    # Simple mock summary - just return the first sentence
-    return text.split('.')[0] + "."
+    
+    if summarizer is None:
+        # Fallback to simple summary if model failed to load
+        return text.split('.')[0] + "."
+    
+    try:
+        # Generate summary using the model
+        summary = summarizer(text, max_length=60, min_length=20, do_sample=False)
+        return summary[0]['summary_text']
+    except Exception as e:
+        print(f"Error generating summary: {e}")
+        # Fallback to simple summary if model fails
+        return f"Error generating summary. Fallback: {text.split('.')[0]}."
 
 def answer_question(content_id, question):
     """Generate a mock answer for demonstration"""
@@ -34,7 +55,7 @@ def answer_question(content_id, question):
     
     text = sample_content[content_id]["text"]
     
-    # Very simple mock QA system - check if key words from question appear in text
+    # Simple mock QA system
     question_words = set(question.lower().split())
     important_words = [word for word in question_words if len(word) > 3 and word not in ["what", "when", "where", "which", "how", "this", "that", "with", "from", "have", "about"]]
     
@@ -83,7 +104,7 @@ with demo:
         
         with gr.Tab("Summarize"):
             summary_button = gr.Button("Generate Summary")
-            summary_output = gr.Textbox(label="Summary", lines=2)
+            summary_output = gr.Textbox(label="Summary", lines=3)
             
             summary_button.click(
                 fn=get_summary,

requirements.txt

@@ -1,3 +1,4 @@
-transformers
 gradio==5.29.0
-firebase-admin
+transformers==4.28.0
+torch==2.0.0
+sentencepiece