Upload folder using huggingface_hub

README.md

@@ -10,7 +10,7 @@ sdk_version: 5.38.1
 
 # AnkiGen - Anki Card Generator
 
-AnkiGen is a Gradio-based web application that generates high-quality Anki flashcards using OpenAI's GPT models. It creates CSV and `.apkg` deck files with intelligent subject-specific card generation and quality review.
+AnkiGen is a Gradio-based web application that generates Anki flashcards using OpenAI's GPT models. It creates CSV and `.apkg` deck files with subject-specific card generation.
 
 ## Features
 

ankigen_core/agents/integration.py

@@ -72,7 +72,7 @@ class AgentOrchestrator:
                     context7_client = Context7Client()
 
                     # Dynamic token allocation based on card generation needs
-                    # More cards need more comprehensive documentation
+                    # More cards need more thorough documentation
                     base_tokens = 8000  # Increased base from 5000
                     if num_cards > 40:
                         token_limit = 12000  # Large card sets need more context

ankigen_core/agents/templates/generators.j2

@@ -2,7 +2,7 @@
 {
     "subject_expert": {
         "name": "subject_expert",
-        "instructions": "You are a world-class expert in {{ subject | default('the subject area') }} with deep pedagogical knowledge. \nYour role is to generate high-quality flashcards that demonstrate mastery of {{ subject | default('the subject') }} concepts.\n\nKey responsibilities:\n- Create ATOMIC cards: extremely short (1-9 words on back), break complex info into multiple simple cards\n- Use standardized, bland prompts without fancy formatting or unusual words\n- Design prompts that match real-life recall situations\n- Put ALL to-be-learned information on the BACK of cards, never in prompts\n- Ensure technical accuracy and depth appropriate for the target level\n- Use domain-specific terminology correctly\n- Connect concepts to prerequisite knowledge\n\nCard Types:\n- Basic cards (card_type='basic'): Standard Q&A format for concepts and facts\n- Cloze cards (card_type='cloze'): Fill-in-the-blank format using {{c1::answer}} syntax for code, syntax, formulas\n\nFor cloze cards, wrap the answer in {{c1::text}} format. Example: 'The vLLM class for inference is {{c1::LLM}}'\n\nPrioritize atomic simplicity over comprehensive single cards. Generate cards that test understanding through simple, direct recall.",
+        "instructions": "You are an expert in {{ subject | default('the subject area') }} with deep pedagogical knowledge. \nYour role is to generate flashcards that demonstrate mastery of {{ subject | default('the subject') }} concepts.\n\nKey responsibilities:\n- Create ATOMIC cards: extremely short (1-9 words on back), break complex info into multiple simple cards\n- Use standardized, bland prompts without fancy formatting or unusual words\n- Design prompts that match real-life recall situations\n- Put ALL to-be-learned information on the BACK of cards, never in prompts\n- Ensure technical accuracy and depth appropriate for the target level\n- Use domain-specific terminology correctly\n- Connect concepts to prerequisite knowledge\n\nCard Types:\n- Basic cards (card_type='basic'): Standard Q&A format for concepts and facts\n- Cloze cards (card_type='cloze'): Fill-in-the-blank format using {{ '{{c1::answer}}' }} syntax for code, syntax, formulas\n\nFor cloze cards, wrap the answer in {{ '{{c1::text}}' }} format. Example: 'The vLLM class for inference is {{ '{{c1::LLM}}' }}'\n\nPrioritize atomic simplicity - break complex info into multiple simple cards. Generate cards that test understanding through simple, direct recall.",
         "model": "{{ subject_expert_model }}",
         "temperature": 0.7,
         "timeout": 120.0,

ankigen_core/auto_config.py

@@ -17,9 +17,9 @@ class AutoConfigService:
     async def analyze_subject(
         self, subject: str, openai_client: AsyncOpenAI
     ) -> AutoConfigSchema:
-        """Analyze a subject string and return optimal configuration settings"""
+        """Analyze a subject string and return configuration settings"""
 
-        system_prompt = """You are an expert educational content analyzer specializing in spaced repetition learning. Analyze the given subject and determine optimal flashcard generation settings that focus on ESSENTIAL, HIGH-VALUE concepts.
+        system_prompt = """You are an educational content analyzer specializing in spaced repetition learning. Analyze the given subject and determine flashcard generation settings that focus on ESSENTIAL concepts.
 
 CRITICAL PRINCIPLE: Quality over quantity. Focus on fundamental concepts that unlock understanding, not trivial facts.
 
@@ -29,11 +29,11 @@ Consider:
    - "Basic Pandas Dataframe" → documentation_focus: "dataframe basics, creation, indexing"
    - "React hooks tutorial" → documentation_focus: "hooks, useState, useEffect"
    - "Docker networking" → documentation_focus: "networking, network drivers, container communication"
-3. Identify the scope: narrow (specific feature), medium (several related topics), broad (comprehensive overview)
+3. Identify the scope: narrow (specific feature), medium (several related topics), broad (full overview)
 4. Determine content type: concepts (theory/understanding), syntax (code/commands), api (library usage), practical (hands-on skills)
-5. Suggest optimal number of topics and cards - aim for comprehensive learning (30-60 total cards minimum)
+5. Suggest number of topics and cards - aim for thorough learning (30-60 total cards minimum)
 6. Recommend cloze cards for syntax/code, basic cards for concepts
-7. Choose model based on complexity: gpt-4.1 for complex/advanced, gpt-4.1-nano for basic/simple
+7. Choose model based on complexity: gpt-4.1 for complex topics, gpt-4.1-nano for basic/simple
 
 IMPORTANT - Focus on HIGH-VALUE topics:
 - GOOD topics: Core concepts, fundamental principles, mental models, design patterns, key abstractions
@@ -45,18 +45,18 @@ Guidelines for settings (MINIMUM 30 cards total):
 - Medium scope: 5-7 core topics with 7-9 cards each (35-63 cards)
 - Broad scope: 6-8 fundamental topics with 6-8 cards each (36-64 cards)
 - "Basic"/"Introduction" keywords: Start with fundamentals, 40-50 cards total
-- "Advanced"/"Complex" keywords: Deep dive into critical concepts, 45-60 cards
+- "Complex" keywords: Deep dive into critical concepts, 45-60 cards
 
 Learning preference suggestions:
 - For basics: "Focus on fundamental concepts and mental models that form the foundation"
 - For practical: "Emphasize core patterns and principles with real-world applications"
 - For theory: "Build deep conceptual understanding with progressive complexity"
 
-Documentation focus examples (be specific and comprehensive):
+Documentation focus examples (be specific and thorough):
 - "Basic Pandas Dataframe" → "dataframe creation, indexing, selection, basic operations, data types"
 - "React hooks" → "useState, useEffect, custom hooks, hook rules, common patterns"
 - "Docker basics" → "containers, images, Dockerfile, volumes, basic networking"
-- "Advanced TypeScript" → "generics, conditional types, mapped types, utility types, type inference"
+- "TypeScript types" → "generics, conditional types, mapped types, utility types, type inference"
 
 Return a JSON object matching the AutoConfigSchema."""
 
@@ -65,7 +65,7 @@ Return a JSON object matching the AutoConfigSchema."""
 Extract:
 1. The library name if mentioned
 2. The specific documentation focus (what aspects of the library to focus on)
-3. Optimal settings for effective learning
+3. Suggested settings for effective learning
 
 Provide a brief rationale for your choices."""
 

app.py

@@ -182,8 +182,8 @@ def create_ankigen_interface():
         js=js_storage,
     ) as ankigen:
         with gr.Column(elem_classes="contain"):
-            gr.Markdown("# 📚 AnkiGen - Advanced Anki Card Generator")
-            gr.Markdown("#### Generate comprehensive Anki flashcards using AI.")
+            gr.Markdown("# 📚 AnkiGen - Anki Card Generator")
+            gr.Markdown("#### Generate Anki flashcards using AI.")
 
             with gr.Accordion("Configuration Settings", open=True):
                 with gr.Row():