app.py

import os
import re
import json
import time
import gradio as gr
import tempfile
from typing import Dict, Any, List, Optional
from transformers import AutoTokenizer
from sentence_transformers import SentenceTransformer
from pydantic import BaseModel, Field
from anthropic import Anthropic


CLAUDE_MODEL = "claude-3-5-sonnet-20241022"
OPENAI_MODEL = "gpt-4o"
GEMINI_MODEL = "gemini-2.0-flash"

DEFAULT_TEMPERATURE = 0.7

TOKENIZER_MODEL = "answerdotai/ModernBERT-base"
SENTENCE_TRANSFORMER_MODEL = "all-MiniLM-L6-v2"

class CourseInfo(BaseModel):
    course_name: str = Field(description="Name of the course")
    section_name: str = Field(description="Name of the course section")
    lesson_name: str = Field(description="Name of the lesson")

class QuizOption(BaseModel):
    text: str = Field(description="The text of the answer option")
    correct: bool = Field(description="Whether this option is correct")

class QuizQuestion(BaseModel):
    question: str = Field(description="The text of the quiz question")
    options: List[QuizOption] = Field(description="List of answer options")

class Segment(BaseModel):
    segment_number: int = Field(description="The segment number")
    topic_name: str = Field(description="Unique and specific topic name that clearly differentiates it from other segments")
    key_concepts: List[str] = Field(description="3-5 key concepts discussed in the segment")
    summary: str = Field(description="Brief summary of the segment (3-5 sentences)")
    quiz_questions: List[QuizQuestion] = Field(description="5 quiz questions based on the segment content")

class TextSegmentAnalysis(BaseModel):
    course_info: CourseInfo = Field(description="Information about the course")
    segments: List[Segment] = Field(description="List of text segments with analysis")


hf_token = os.environ.get('HF_TOKEN', None)
login(token=hf_token)

tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_MODEL)
sentence_model = SentenceTransformer(SENTENCE_TRANSFORMER_MODEL)

# System prompt
system_prompt = """You are an expert educational content analyzer. Your task is to analyze text content, 
identify distinct segments, and create high-quality educational quiz questions for each segment."""

def clean_text(text):
    text = re.sub(r'\[speaker_\d+\]', '', text)
    text = re.sub(r'\s+', ' ', text).strip()
    return text

def split_text_by_tokens(text, max_tokens=8000):
    text = clean_text(text)
    tokens = tokenizer.encode(text)
    
    if len(tokens) <= max_tokens:
        return [text]
    
    split_point = len(tokens) // 2
    
    sentences = re.split(r'(?<=[.!?])\s+', text)
    
    first_half = []
    second_half = []
    
    current_tokens = 0
    for sentence in sentences:
        sentence_tokens = len(tokenizer.encode(sentence))
        
        if current_tokens + sentence_tokens <= split_point:
            first_half.append(sentence)
            current_tokens += sentence_tokens
        else:
            second_half.append(sentence)
    
    return [" ".join(first_half), " ".join(second_half)]



def generate_with_claude(text, api_key, course_name="", section_name="", lesson_name=""):
    from prompts import SYSTEM_PROMPT, ANALYSIS_PROMPT_TEMPLATE_CLAUDE

    client = Anthropic(api_key=api_key)
    
    segment_analysis_schema = TextSegmentAnalysis.model_json_schema()
    
    tools = [
        {
            "name": "build_segment_analysis",
            "description": "Build the text segment analysis with quiz questions",
            "input_schema": segment_analysis_schema
        }
    ]
    
    system_prompt = """You are a helpful assistant specialized in text analysis and educational content creation. 
    You analyze texts to identify distinct segments, create summaries, and generate quiz questions."""
    
    prompt =     prompt = ANALYSIS_PROMPT_TEMPLATE_CLAUDE.format(
        course_name=course_name,
        section_name=section_name,
        lesson_name=lesson_name,
        text=text
    )

    try:
        response = client.messages.create(
            model=CLAUDE_MODEL,
            max_tokens=8192,
            temperature=DEFAULT_TEMPERATURE,
            system=system_prompt,
            messages=[
                {
                    "role": "user",
                    "content": prompt
                }
            ],
            tools=tools,
            tool_choice={"type": "tool", "name": "build_segment_analysis"}
        )
        
        # Extract the tool call content
        if response.content and len(response.content) > 0 and hasattr(response.content[0], 'input'):
            function_call = response.content[0].input
            return function_call
        else:
            raise Exception("No valid tool call found in the response")
    except Exception as e:
        raise Exception(f"Error calling Anthropic API: {str(e)}")




def get_llm_by_api_key(api_key):
    if api_key.startswith("sk-ant-"):  # Claude API key format
        from langchain_anthropic import ChatAnthropic
        return ChatAnthropic(
            anthropic_api_key=api_key,
            model_name=CLAUDE_MODEL,
            temperature=DEFAULT_TEMPERATURE,
            max_retries=3
        )
    elif api_key.startswith("sk-"):  # OpenAI API key format
        from langchain_openai import ChatOpenAI
        return ChatOpenAI(
            openai_api_key=api_key,
            model_name=OPENAI_MODEL,
            temperature=DEFAULT_TEMPERATURE,
            max_retries=3
        )
    else:  # Default to Gemini
        from langchain_google_genai import ChatGoogleGenerativeAI
        os.environ["GOOGLE_API_KEY"] = api_key
        return ChatGoogleGenerativeAI(
            model=GEMINI_MODEL,
            temperature=DEFAULT_TEMPERATURE,
            max_retries=3
        )

def segment_and_analyze_text(text: str, api_key: str, course_name="", section_name="", lesson_name="") -> Dict[str, Any]:
    from prompts import SYSTEM_PROMPT, ANALYSIS_PROMPT_TEMPLATE_GEMINI
    if api_key.startswith("sk-ant-"):
        return generate_with_claude(text, api_key, course_name, section_name, lesson_name)
    
    # For other models, use LangChain
    llm = get_llm_by_api_key(api_key)
    
    prompt = ANALYSIS_PROMPT_TEMPLATE_GEMINI.format(
        course_name=course_name,
        section_name=section_name,
        lesson_name=lesson_name,
        text=text
    )

    try:
        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": prompt}
        ]
        
        response = llm.invoke(messages)
        
        try:
            content = response.content
            json_match = re.search(r'```json\s*([\s\S]*?)\s*```', content)
            
            if json_match:
                json_str = json_match.group(1)
            else:
                json_match = re.search(r'(\{[\s\S]*\})', content)
                if json_match:
                    json_str = json_match.group(1)
                else:
                    json_str = content
            
            # Parse the JSON
            function_call = json.loads(json_str)
            return function_call
        except json.JSONDecodeError:
            raise Exception("Could not parse JSON from LLM response")
    except Exception as e:
        raise Exception(f"Error calling API: {str(e)}")

def format_quiz_for_display(results):
    output = []
    
    if "course_info" in results:
        course_info = results["course_info"]
        output.append(f"{'='*40}")
        output.append(f"COURSE: {course_info.get('course_name', 'N/A')}")
        output.append(f"SECTION: {course_info.get('section_name', 'N/A')}")
        output.append(f"LESSON: {course_info.get('lesson_name', 'N/A')}")
        output.append(f"{'='*40}\n")
    
    segments = results.get("segments", [])
    for i, segment in enumerate(segments):
        topic = segment["topic_name"]
        segment_num = i + 1 
        output.append(f"\n\n{'='*40}")
        output.append(f"SEGMENT {segment_num}: {topic}")
        output.append(f"{'='*40}\n")
        output.append("KEY CONCEPTS:")
        for concept in segment["key_concepts"]:
            output.append(f"• {concept}")
        output.append("\nSUMMARY:")
        output.append(segment["summary"])
        output.append("\nQUIZ QUESTIONS:")
        for i, q in enumerate(segment["quiz_questions"]):
            output.append(f"\n{i+1}. {q['question']}")
            for j, option in enumerate(q['options']):
                letter = chr(97 + j).upper()
                correct_marker = " ✓" if option["correct"] else ""
                output.append(f"   {letter}. {option['text']}{correct_marker}")
    return "\n".join(output)

def analyze_document(text, api_key, course_name, section_name, lesson_name):
    try:
        start_time = time.time()
        
        # Split text if it's too long
        text_parts = split_text_by_tokens(text)
        
        all_results = {
            "course_info": {
                "course_name": course_name,
                "section_name": section_name,
                "lesson_name": lesson_name
            },
            "segments": []
        }
        segment_counter = 1
        
        # Process each part of the text
        for part in text_parts:
            analysis = segment_and_analyze_text(
                part, 
                api_key,
                course_name=course_name, 
                section_name=section_name, 
                lesson_name=lesson_name
            )
            
            if "segments" in analysis:
                for segment in analysis["segments"]:
                    segment["segment_number"] = segment_counter
                    all_results["segments"].append(segment)
                    segment_counter += 1
        
        end_time = time.time()
        total_time = end_time - start_time
        
        # Format the results for display
        formatted_text = format_quiz_for_display(all_results)
        formatted_text = f"Total processing time: {total_time:.2f} seconds\n\n" + formatted_text
        
        # Create temporary files for JSON and text output
        json_path = tempfile.mktemp(suffix='.json')
        with open(json_path, 'w', encoding='utf-8') as json_file:
            json.dump(all_results, json_file, indent=2)
        
        txt_path = tempfile.mktemp(suffix='.txt')
        with open(txt_path, 'w', encoding='utf-8') as txt_file:
            txt_file.write(formatted_text)
            
        return formatted_text, json_path, txt_path
    except Exception as e:
        error_message = f"Error processing document: {str(e)}"
        return error_message, None, None

with gr.Blocks(title="Quiz Generator") as app:
    gr.Markdown("# Quiz Generator")

    with gr.Row():
        with gr.Column():
            course_name = gr.Textbox(
                placeholder="Enter the course name",
                label="Course Name"
            )
            section_name = gr.Textbox(
                placeholder="Enter the section name",
                label="Section Name"
            )
            lesson_name = gr.Textbox(
                placeholder="Enter the lesson name",
                label="Lesson Name"
            )

    with gr.Row():
        with gr.Column():
            input_text = gr.Textbox(
                label="Input Document Text",
                placeholder="Paste your document text here...",
                lines=10
            )
            
            api_key = gr.Textbox(
                label="API Key",
                placeholder="Enter your OpenAI, Claude, or Gemini API key",
                type="password"
            )
            
            analyze_btn = gr.Button("Analyze Document")
            
        with gr.Column():
            output_results = gr.Textbox(
                label="Analysis Results",
                lines=20
            )
            json_file_output = gr.File(label="Download JSON")
            txt_file_output = gr.File(label="Download TXT")
    
    analyze_btn.click(
        fn=analyze_document,
        inputs=[input_text, api_key, course_name, section_name, lesson_name],
        outputs=[output_results, json_file_output, txt_file_output]
    )
            
if __name__ == "__main__":
    app.launch()