finish the idea of micro learning part 3

README.md

@@ -10,4 +10,21 @@ pinned: false
 short_description: micro learning
 ---
 
+
+/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
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

ai/agents.py

@@ -0,0 +1,39 @@
+# In ai/agents.py
+class Agent:
+    def __init__(self, name, role):
+        self.name = name
+        self.role = role
+    
+    def process(self, message, context=None):
+        raise NotImplementedError("Agents must implement process method")
+
+class TutorAgent(Agent):
+    def __init__(self, hf_service):
+        super().__init__("Tutor", "Explains concepts")
+        self.hf = hf_service
+    
+    def process(self, message, context=None):
+        # Use Hugging Face to generate explanations
+        return f"Let me explain: {message}"
+
+class QuizAgent(Agent):
+    def __init__(self, hf_service):
+        super().__init__("Quiz", "Generates questions")
+        self.hf = hf_service
+    
+    def process(self, content, context=None):
+        # Generate quiz questions based on content
+        return ["Question 1: ...?", "Question 2: ...?"]
+
+class AgentCoordinator:
+    def __init__(self, hf_service):
+        self.hf = hf_service
+        self.agents = {
+            "tutor": TutorAgent(hf_service),
+            "quiz": QuizAgent(hf_service)
+        }
+    
+    def dispatch(self, agent_type, message, context=None):
+        if agent_type in self.agents:
+            return self.agents[agent_type].process(message, context)
+        return "Agent not found"

ai/huggingface.py

@@ -0,0 +1,22 @@
+# In /ai/huggingface.py
+import os
+from transformers import pipeline
+
+class HuggingFaceService:
+    def __init__(self):
+        # Load models once during initialization
+        self.summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+        self.qa_model = pipeline("question-answering", model="deepset/roberta-base-squad2")
+        self.classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+    
+    def summarize_content(self, content, max_length=100):
+        """Generate a short summary of learning content"""
+        return self.summarizer(content, max_length=max_length, min_length=30, do_sample=False)
+    
+    def answer_question(self, question, context):
+        """Answer a question based on the learning content"""
+        return self.qa_model(question=question, context=context)
+    
+    def classify_content(self, content, labels=["beginner", "intermediate", "advanced"]):
+        """Classify content by difficulty level"""
+        return self.classifier(content, candidate_labels=labels)

api/agents.py

@@ -0,0 +1,35 @@
+# Enhanced QuizAgent in ai/agents.py
+class QuizAgent(Agent):
+    def __init__(self, hf_service):
+        super().__init__("Quiz", "Generates questions")
+        self.hf = hf_service
+    
+    def process(self, content, context=None):
+        # Generate 3-5 questions based on content
+        questions = []
+        
+        # Extract key concepts using summarization
+        summary = self.hf.summarize_content(content)[0]['summary_text']
+        
+        # Generate questions using question-answering in reverse
+        # We'll extract potential answers and create questions for them
+        sentences = summary.split('. ')
+        for sentence in sentences[:5]:  # Limit to 5 questions
+            # Use the sentence as context and try to generate a question
+            potential_answer = sentence.strip()
+            
+            # We'll need to integrate with a better question generation model here
+            # For now, create a simple question by masking parts of the sentence
+            words = potential_answer.split()
+            if len(words) > 5:
+                # Find a key noun or entity to ask about
+                # This is simplified - would need NER or POS tagging in production
+                question_word = words[len(words)//2]
+                question = potential_answer.replace(question_word, "___")
+                questions.append({
+                    "question": f"Complete the following: {question}", 
+                    "answer": question_word,
+                    "context": potential_answer
+                })
+        
+        return questions

api/main.py

@@ -0,0 +1,114 @@
+# In api/main.py
+from fastapi import FastAPI, HTTPException, Depends
+from pydantic import BaseModel
+from typing import List, Optional
+import sys
+import os
+
+# Add parent directory to path to import modules
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from ai.huggingface import HuggingFaceService
+from ai.agents import AgentCoordinator
+from data.content import ContentManager
+from data.users import UserManager
+
+app = FastAPI(
+    title="Micro Learning API",
+    description="API for microlearning content and personalization",
+    version="0.1.0"
+)
+
+# Initialize services
+hf_service = HuggingFaceService()
+agent_coordinator = AgentCoordinator(hf_service)
+content_manager = ContentManager()
+user_manager = UserManager()
+
+# Models
+class ContentBase(BaseModel):
+    title: str
+    text: str
+    tags: List[str]
+    
+class ContentCreate(ContentBase):
+    pass
+    
+class Content(ContentBase):
+    id: str
+    
+class UserBase(BaseModel):
+    name: str
+    email: str
+    
+class UserCreate(UserBase):
+    pass
+    
+class User(UserBase):
+    id: str
+    progress: dict = {}
+    
+class ProgressUpdate(BaseModel):
+    module_id: str
+    completion: float  # 0.0 to 1.0
+    
+# Routes
+@app.get("/content/{content_id}", response_model=Content)
+async def get_content(content_id: str):
+    content = content_manager.get_by_id(content_id)
+    if not content:
+        raise HTTPException(status_code=404, detail="Content not found")
+    return content
+
+@app.post("/content/", response_model=str)
+async def create_content(content: ContentCreate):
+    content_id = content_manager.save_content(content.dict())
+    return content_id
+
+@app.get("/content/{content_id}/summary")
+async def get_summary(content_id: str):
+    content = content_manager.get_by_id(content_id)
+    if not content:
+        raise HTTPException(status_code=404, detail="Content not found")
+    
+    summary = hf_service.summarize_content(content['text'])
+    return {"summary": summary[0]['summary_text']}
+
+@app.post("/content/{content_id}/ask")
+async def ask_question(content_id: str, question: str):
+    content = content_manager.get_by_id(content_id)
+    if not content:
+        raise HTTPException(status_code=404, detail="Content not found")
+    
+    answer = hf_service.answer_question(question, content['text'])
+    return {"answer": answer['answer'], "confidence": answer['score']}
+
+@app.get("/content/{content_id}/quiz")
+async def generate_quiz(content_id: str):
+    content = content_manager.get_by_id(content_id)
+    if not content:
+        raise HTTPException(status_code=404, detail="Content not found")
+    
+    questions = agent_coordinator.dispatch("quiz", content['text'])
+    return {"questions": questions}
+
+@app.post("/users/", response_model=str)
+async def create_user(user: UserCreate):
+    user_id = user_manager.create_user(user.dict())
+    return user_id
+
+@app.get("/users/{user_id}", response_model=User)
+async def get_user(user_id: str):
+    user = user_manager.get_user(user_id)
+    if not user:
+        raise HTTPException(status_code=404, detail="User not found")
+    return user
+
+@app.post("/users/{user_id}/progress")
+async def update_progress(user_id: str, update: ProgressUpdate):
+    user = user_manager.get_user(user_id)
+    if not user:
+        raise HTTPException(status_code=404, detail="User not found")
+        
+    user_manager.update_progress(user_id, update.module_id, update.completion)
+    return {"status": "updated"}

app.py

@@ -1 +1,117 @@
-# Micro-Learning Platform
+# Updated app.py for Hugging Face Spaces compatibility
+import gradio as gr
+from ai.huggingface import HuggingFaceService
+from data.content import ContentManager
+import os
+
+# Initialize services - with error handling
+try:
+    hf_service = HuggingFaceService()
+    content_manager = ContentManager()
+except Exception as e:
+    print(f"Initialization error: {e}")
+    # Create mock services for demo if real ones fail
+    class MockHFService:
+        def summarize_content(self, content, max_length=100):
+            return [{"summary_text": "This is a mock summary for demo purposes."}]
+        def answer_question(self, question, context):
+            return {"answer": "This is a mock answer for demo purposes.", "score": 0.95}
+        def classify_content(self, content, labels=None):
+            return {"labels": ["beginner"], "scores": [0.9]}
+    
+    class MockContentManager:
+        def get_by_id(self, content_id):
+            return {"title": "Sample Module", "text": "This is sample content for the microlearning demo."}
+    
+    hf_service = MockHFService()
+    content_manager = MockContentManager()
+
+# Demo content for testing
+sample_content = {
+    "sample1": {"title": "Introduction to AI", "text": "Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems."},
+    "sample2": {"title": "Python Basics", "text": "Python is a high-level, interpreted programming language known for its readability and simplicity."}
+}
+
+# Helper functions
+def get_content_list():
+    return [{"id": k, "title": v["title"]} for k, v in sample_content.items()]
+
+def display_content(content_id):
+    if content_id in sample_content:
+        return sample_content[content_id]["text"]
+    
+    # Try to get from real content manager if demo content not found
+    content = content_manager.get_by_id(content_id)
+    if content and "text" in content:
+        return content["text"]
+    return "Content not found"
+
+def summarize_content(content_id):
+    if content_id in sample_content:
+        text = sample_content[content_id]["text"]
+    else:
+        content = content_manager.get_by_id(content_id)
+        if not content or "text" not in content:
+            return "Content not found"
+        text = content["text"]
+    
+    summary = hf_service.summarize_content(text)
+    return summary[0]['summary_text']
+
+def answer_question(content_id, question):
+    if content_id in sample_content:
+        text = sample_content[content_id]["text"]
+    else:
+        content = content_manager.get_by_id(content_id)
+        if not content or "text" not in content:
+            return "Content not found"
+        text = content["text"]
+    
+    answer = hf_service.answer_question(question, text)
+    return f"{answer['answer']} (confidence: {answer['score']:.2f})"
+
+# Gradio interface
+demo = gr.Blocks(title="Micro Learning Platform")
+
+with demo:
+    gr.Markdown("# Micro Learning Platform")
+    
+    with gr.Tab("Browse Content"):
+        gr.Markdown("## Available Learning Modules")
+        content_dropdown = gr.Dropdown(choices=get_content_list(), label="Select Module", value="sample1")
+        content_display = gr.Textbox(label="Content", lines=5)
+        view_button = gr.Button("View Content")
+        
+        view_button.click(
+            display_content, 
+            inputs=[content_dropdown], 
+            outputs=[content_display]
+        )
+    
+    with gr.Tab("Study Tools"):
+        with gr.Row():
+            study_content_dropdown = gr.Dropdown(choices=get_content_list(), label="Select Module", value="sample1")
+        
+        with gr.Tab("Summarize"):
+            summarize_button = gr.Button("Summarize")
+            summary_output = gr.Textbox(label="Summary", lines=3)
+            
+            summarize_button.click(
+                summarize_content, 
+                inputs=[study_content_dropdown], 
+                outputs=[summary_output]
+            )
+        
+        with gr.Tab("Ask Question"):
+            question_input = gr.Textbox(label="Your Question")
+            ask_button = gr.Button("Ask")
+            answer_output = gr.Textbox(label="Answer")
+            
+            ask_button.click(
+                answer_question, 
+                inputs=[study_content_dropdown, question_input], 
+                outputs=[answer_output]
+            )
+
+# This is required for Hugging Face Spaces
+demo.launch()