finish the idea of micro learning part 5

app.py

@@ -1,127 +1,31 @@
-# Improved app.py with real summarization using Hugging Face model
+# Super minimal app.py for troubleshooting
 import gradio as gr
-from transformers import pipeline
+import sys
 
-# 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
+# Print Python version and loaded modules for debugging
+print(f"Python version: {sys.version}")
+print("Loaded modules:")
+for name, module in sys.modules.items():
+    if hasattr(module, "__version__"):
+        print(f"  {name}: {module.__version__}")
 
-# 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. 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. 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. 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."
-    }
-}
+# Simple function that doesn't require any ML models
+def echo(text):
+    return f"You said: {text}"
 
-# Function to generate real summaries using Hugging Face model
-def get_summary(content_id):
-    """Generate a summary using a HuggingFace model"""
-    if content_id not in sample_content:
-        return "Content not found"
-    
-    text = sample_content[content_id]["text"]
-    
-    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]}."
+# Create a simple Gradio interface
+demo = gr.Interface(
+    fn=echo,
+    inputs=gr.Textbox(placeholder="Type something here..."),
+    outputs=gr.Textbox(),
+    title="Micro Learning Platform - Debug Version",
+    description="This is a minimal version for troubleshooting"
+)
 
-def answer_question(content_id, question):
-    """Generate a mock answer for demonstration"""
-    if content_id not in sample_content:
-        return "Content not found"
-    
-    text = sample_content[content_id]["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"]]
-    
-    if any(word in text.lower() for word in important_words):
-        return f"Based on the content, I can tell you that the answer relates to {sample_content[content_id]['title']}."
-    else:
-        return "I don't have enough information to answer that question based on the selected content."
-
-# Create the Gradio interface
-demo = gr.Blocks(title="Micro Learning Platform")
-
-with demo:
-    gr.Markdown("# Micro Learning Platform")
-    gr.Markdown("## A demonstration of microlearning concepts with AI")
-    
-    with gr.Tab("Browse Content"):
-        content_dropdown = gr.Dropdown(
-            choices=[{"value": k, "label": v["title"]} for k, v in sample_content.items()],
-            value="module1",
-            label="Select a learning module"
-        )
-        
-        content_display = gr.Textbox(
-            value=sample_content["module1"]["text"],
-            label="Module content",
-            lines=5,
-            interactive=False
-        )
-        
-        def update_content(module_id):
-            return sample_content.get(module_id, {"text": "Content not found"})["text"]
-        
-        content_dropdown.change(
-            fn=update_content,
-            inputs=content_dropdown,
-            outputs=content_display
-        )
-    
-    with gr.Tab("Study Tools"):
-        with gr.Row():
-            study_content_dropdown = gr.Dropdown(
-                choices=[{"value": k, "label": v["title"]} for k, v in sample_content.items()],
-                value="module1",
-                label="Select a learning module"
-            )
-        
-        with gr.Tab("Summarize"):
-            summary_button = gr.Button("Generate Summary")
-            summary_output = gr.Textbox(label="Summary", lines=3)
-            
-            summary_button.click(
-                fn=get_summary,
-                inputs=study_content_dropdown,
-                outputs=summary_output
-            )
-        
-        with gr.Tab("Ask Question"):
-            question_input = gr.Textbox(label="Your Question", placeholder="Type your question here...")
-            ask_button = gr.Button("Submit Question")
-            answer_output = gr.Textbox(label="Answer", lines=3)
-            
-            ask_button.click(
-                fn=answer_question,
-                inputs=[study_content_dropdown, question_input],
-                outputs=answer_output
-            )
+# Add a clear print statement when the app starts
+print("Application starting...")
 
 # Launch the application
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()
+    print("Application launched successfully!")