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import gradio as gr
import PyPDF2
import docx
from openai import OpenAI
import io
import json
import time
from typing import List, Dict, Any, Optional
import spaces
# Global variables to store API key and document text
API_KEY = ""
DOCUMENT_TEXT = ""
MODEL = "google/gemma-3-27b-it:free"
def setup_client(api_key: str):
"""Initialize and test API key"""
global API_KEY
try:
# Test the API key by creating a client and making a simple request
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=api_key,
)
# Store the API key globally
API_KEY = api_key
return "β
API Key configured successfully!"
except Exception as e:
return f"β Error configuring API: {str(e)}"
def create_client() -> Optional[OpenAI]:
"""Create OpenAI client with stored API key"""
if not API_KEY:
return None
return OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=API_KEY,
)
def extract_text_from_pdf(file_path: str) -> str:
"""Extract text from PDF file"""
try:
with open(file_path, 'rb') as file:
pdf_reader = PyPDF2.PdfReader(file)
text = ""
for page in pdf_reader.pages:
text += page.extract_text() + "\n"
return text
except Exception as e:
return f"Error reading PDF: {str(e)}"
def extract_text_from_docx(file_path: str) -> str:
"""Extract text from DOCX file"""
try:
doc = docx.Document(file_path)
text = ""
for paragraph in doc.paragraphs:
text += paragraph.text + "\n"
return text
except Exception as e:
return f"Error reading DOCX: {str(e)}"
@spaces.GPU
def process_document(file):
"""Process uploaded document and extract text"""
global DOCUMENT_TEXT
if file is None:
DOCUMENT_TEXT = "" # Reset if no file
return "β No file uploaded"
try:
file_path = file.name
file_extension = file_path.lower().split('.')[-1]
if file_extension == 'pdf':
extracted_text = extract_text_from_pdf(file_path)
elif file_extension in ['docx', 'doc']:
extracted_text = extract_text_from_docx(file_path)
else:
DOCUMENT_TEXT = "" # Reset on unsupported format
return "β Unsupported file format. Please upload PDF or DOCX files."
# Check if extraction was successful
if extracted_text.startswith("Error"):
DOCUMENT_TEXT = "" # Reset on error
return extracted_text
# Set the global variable
DOCUMENT_TEXT = extracted_text.strip()
if DOCUMENT_TEXT and len(DOCUMENT_TEXT) > 0:
word_count = len(DOCUMENT_TEXT.split())
preview = DOCUMENT_TEXT[:200] + "..." if len(DOCUMENT_TEXT) > 200 else DOCUMENT_TEXT
return f"β
Document processed successfully!\nπ Word count: {word_count}\nπ Preview: {preview}"
else:
DOCUMENT_TEXT = "" # Reset if no text extracted
return "β Could not extract text from the document. The document might be empty or contain only images."
except Exception as e:
DOCUMENT_TEXT = "" # Reset on any error
return f"β Error processing document: {str(e)}"
@spaces.GPU
def generate_content(prompt: str, max_tokens: int = 2000) -> str:
"""Generate content using the AI model"""
global DOCUMENT_TEXT
if not API_KEY:
return "β Please configure your API key first"
if not DOCUMENT_TEXT or len(DOCUMENT_TEXT.strip()) == 0:
return "β Please upload and process a document first. Make sure the document contains readable text."
try:
client = create_client()
if not client:
return "β Failed to create API client"
completion = client.chat.completions.create(
extra_headers={
"HTTP-Referer": "https://educational-assistant.app",
"X-Title": "Educational Content Creator",
},
model=MODEL,
messages=[
{
"role": "system",
"content": "You are an expert educational content creator. Create comprehensive, engaging, and pedagogically sound educational materials based on the provided document content."
},
{
"role": "user",
"content": f"Document Content:\n{DOCUMENT_TEXT}\n\n{prompt}"
}
],
max_tokens=max_tokens,
temperature=0.7
)
return completion.choices[0].message.content
except Exception as e:
return f"β Error generating content: {str(e)}"
@spaces.GPU
def generate_summary():
"""Generate comprehensive summary"""
prompt = """Create a comprehensive summary of this document with the following structure:
## π Executive Summary
Provide a brief overview in 2-3 sentences.
## π― Key Points
List the main concepts, ideas, or arguments presented.
## π Detailed Summary
Provide a thorough summary organized by topics or sections.
## π‘ Important Takeaways
Highlight the most crucial information students should remember.
"""
return generate_content(prompt)
@spaces.GPU
def generate_study_notes():
"""Generate structured study notes"""
prompt = """Create comprehensive study notes from this document with:
## π Study Notes
### π Key Concepts
- Define important terms and concepts
- Explain their significance
### π Main Topics
Organize content into clear sections with:
- Topic headings
- Key points under each topic
- Supporting details and examples
### π§ Memory Aids
- Create mnemonics for complex information
- Suggest visualization techniques
- Provide connection points between concepts
### β‘ Quick Review Points
- Bullet points for rapid review
- Essential facts and figures
"""
return generate_content(prompt)
@spaces.GPU
def generate_quiz():
"""Generate quiz questions"""
prompt = """Create a comprehensive quiz based on this document:
## π Quiz Questions
### Multiple Choice Questions (5 questions)
For each question, provide:
- Clear question
- 4 options (A, B, C, D)
- Correct answer
- Brief explanation
### Short Answer Questions (5 questions)
- Questions requiring 2-3 sentence answers
- Cover key concepts and applications
### Essay Questions (2 questions)
- Thought-provoking questions requiring detailed responses
- Focus on analysis, synthesis, or evaluation
### Answer Key
Provide all correct answers with explanations.
"""
return generate_content(prompt, max_tokens=3000)
@spaces.GPU
def generate_flashcards():
"""Generate flashcards"""
prompt = """Create 15-20 flashcards based on this document:
## π΄ Flashcards
Format each flashcard as:
**Card X:**
**Front:** [Question/Term]
**Back:** [Answer/Definition/Explanation]
Include flashcards for:
- Key terms and definitions
- Important concepts
- Facts and figures
- Cause and effect relationships
- Applications and examples
Make questions clear and answers comprehensive but concise.
"""
return generate_content(prompt, max_tokens=2500)
@spaces.GPU
def generate_mind_map():
"""Generate mind map structure"""
prompt = """Create a detailed mind map structure for this document:
## π§ Mind Map Structure
**Central Topic:** [Main subject of the document]
### Primary Branches:
For each main topic, create branches with:
- **Branch 1:** [Topic Name]
- Sub-branch 1.1: [Subtopic]
- Detail 1.1.1
- Detail 1.1.2
- Sub-branch 1.2: [Subtopic]
- Detail 1.2.1
- Detail 1.2.2
### Connections:
- Identify relationships between different branches
- Note cross-references and dependencies
- Highlight cause-effect relationships
### Visual Elements Suggestions:
- Color coding recommendations
- Symbol suggestions for different types of information
- Emphasis techniques for key concepts
"""
return generate_content(prompt)
@spaces.GPU
def generate_lesson_plan():
"""Generate lesson plan"""
prompt = """Create a detailed lesson plan based on this document:
## π Lesson Plan
### Learning Objectives
By the end of this lesson, students will be able to:
- [Specific, measurable objectives]
### Prerequisites
- Required background knowledge
- Recommended prior reading
### Lesson Structure (60 minutes)
**Introduction (10 minutes)**
- Hook/attention grabber
- Learning objectives overview
**Main Content (35 minutes)**
- Key concepts presentation
- Activities and examples
- Discussion points
**Practice & Application (10 minutes)**
- Practice exercises
- Real-world applications
**Wrap-up & Assessment (5 minutes)**
- Summary of key points
- Quick assessment questions
### Materials Needed
- List of required resources
### Assessment Methods
- How to evaluate student understanding
### Homework/Extension Activities
- Additional practice opportunities
"""
return generate_content(prompt, max_tokens=2500)
@spaces.GPU
def generate_concept_explanations():
"""Generate detailed concept explanations"""
prompt = """Provide detailed explanations of key concepts from this document:
## π Concept Deep Dive
For each major concept, provide:
### Concept Name
**Definition:** Clear, precise definition
**Explanation:** Detailed explanation in simple terms
**Examples:** Real-world examples and applications
**Analogies:** Helpful comparisons to familiar concepts
**Common Misconceptions:** What students often get wrong
**Connection to Other Concepts:** How it relates to other topics
**Practice Application:** Simple exercise or question
---
Repeat this structure for all major concepts in the document.
"""
return generate_content(prompt, max_tokens=3000)
@spaces.GPU
def generate_practice_problems():
"""Generate practice problems"""
prompt = """Create practice problems based on this document:
## πͺ Practice Problems
### Beginner Level (5 problems)
- Basic application of concepts
- Direct recall and simple calculations
- Step-by-step solutions provided
### Intermediate Level (5 problems)
- Multi-step problems
- Requires understanding of relationships
- Guided solutions with explanations
### Advanced Level (3 problems)
- Complex scenarios
- Requires analysis and synthesis
- Detailed solution strategies
### Challenge Problems (2 problems)
- Extension beyond document content
- Creative application
- Multiple solution approaches
**For each problem, include:**
- Clear problem statement
- Required formulas/concepts
- Step-by-step solution
- Common mistakes to avoid
"""
return generate_content(prompt, max_tokens=3500)
def check_document_status():
"""Check if document is loaded"""
global DOCUMENT_TEXT
if DOCUMENT_TEXT and len(DOCUMENT_TEXT.strip()) > 0:
word_count = len(DOCUMENT_TEXT.split())
return f"β
Document loaded ({word_count} words)"
else:
return "β No document loaded"
# Create Gradio interface
def create_interface():
with gr.Blocks(title="π Educational Content Creator Assistant", theme=gr.themes.Soft()) as app:
gr.Markdown("""
# π Educational Content Creator Assistant
Transform your documents into comprehensive educational materials using AI!
**Features:** Study Notes β’ Quizzes β’ Flashcards β’ Mind Maps β’ Lesson Plans β’ Practice Problems & More!
*Powered by ZeroGPU for enhanced performance*
""")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### π Setup")
api_key = gr.Textbox(
label="OpenRouter API Key",
type="password",
placeholder="Enter your OpenRouter API key..."
)
setup_btn = gr.Button("π§ Configure API", variant="primary")
setup_status = gr.Textbox(label="Status", interactive=False)
gr.Markdown("### π Document Upload")
file_upload = gr.File(
label="Upload Document (PDF or DOCX)",
file_types=[".pdf", ".docx", ".doc"]
)
process_btn = gr.Button("π Process Document", variant="secondary")
process_status = gr.Textbox(label="Processing Status", interactive=False)
# Add document status indicator
doc_status = gr.Textbox(label="Document Status", value="β No document loaded", interactive=False)
with gr.Column(scale=2):
gr.Markdown("### π― Generate Educational Content")
with gr.Row():
summary_btn = gr.Button("π Generate Summary", variant="primary")
notes_btn = gr.Button("π Study Notes", variant="primary")
quiz_btn = gr.Button("π Create Quiz", variant="primary")
with gr.Row():
flashcards_btn = gr.Button("π΄ Flashcards", variant="secondary")
mindmap_btn = gr.Button("π§ Mind Map", variant="secondary")
lesson_btn = gr.Button("π Lesson Plan", variant="secondary")
with gr.Row():
concepts_btn = gr.Button("π Concept Explanations", variant="secondary")
problems_btn = gr.Button("πͺ Practice Problems", variant="secondary")
output = gr.Textbox(
label="Generated Content",
lines=20,
max_lines=30,
placeholder="Generated educational content will appear here...",
show_copy_button=True
)
gr.Markdown("""
### π How to Use:
1. **Get API Key:** Sign up at [OpenRouter](https://openrouter.ai/) and get your API key
2. **Configure:** Enter your API key and click "Configure API"
3. **Upload:** Upload a PDF or DOCX document
4. **Process:** Click "Process Document" to extract text
5. **Generate:** Choose any educational content type to generate
### π― Content Types:
- **Summary:** Comprehensive overview with key points
- **Study Notes:** Structured notes with key concepts and memory aids
- **Quiz:** Multiple choice, short answer, and essay questions
- **Flashcards:** Question-answer pairs for memorization
- **Mind Map:** Visual structure of document concepts
- **Lesson Plan:** Complete teaching plan with objectives and activities
- **Concept Explanations:** Deep dive into key concepts with examples
- **Practice Problems:** Graded exercises from beginner to advanced
### β‘ Performance Note:
This app uses ZeroGPU for enhanced processing. Functions will automatically utilize GPU resources when needed.
""")
# Event handlers
setup_btn.click(
setup_client,
inputs=[api_key],
outputs=[setup_status]
)
def process_and_update_status(file):
result = process_document(file)
status = check_document_status()
return result, status
process_btn.click(
process_and_update_status,
inputs=[file_upload],
outputs=[process_status, doc_status]
)
summary_btn.click(
generate_summary,
outputs=[output]
)
notes_btn.click(
generate_study_notes,
outputs=[output]
)
quiz_btn.click(
generate_quiz,
outputs=[output]
)
flashcards_btn.click(
generate_flashcards,
outputs=[output]
)
mindmap_btn.click(
generate_mind_map,
outputs=[output]
)
lesson_btn.click(
generate_lesson_plan,
outputs=[output]
)
concepts_btn.click(
generate_concept_explanations,
outputs=[output]
)
problems_btn.click(
generate_practice_problems,
outputs=[output]
)
# Update document status when page loads
app.load(
check_document_status,
outputs=[doc_status]
)
return app
# Launch the application
if __name__ == "__main__":
app = create_interface()
app.launch() |