app.py

import gradio as gr
import wikipedia
from langchain_tavily import TavilySearch
from transformers import pipeline
from llama_index.llms.nebius import NebiusLLM
from PyPDF2 import PdfReader
from textblob import TextBlob

import os
from dotenv import load_dotenv

load_dotenv() 

os.environ["TAVILY_API_KEY"] = os.getenv("TAVILY_API_KEY")
NEBIUS_API_KEY = os.getenv("NEBIUS_API_KEY")
llm = NebiusLLM(
    api_key=NEBIUS_API_KEY, model="meta-llama/Meta-Llama-3.1-70B-Instruct-fast"
)

summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

def letter_counter(word, letter):
    """
    Count the number of occurrences of a letter in a word or text.

    Args:
        word (str): The input text to search through
        letter (str): The letter to search for

    Returns:
        str: A message indicating how many times the letter appears
    """
    word = word.lower()
    letter = letter.lower()
    count = word.count(letter)
    return count

def web_search(query):
    """
    Web search using TavilySearch, formatted output.
    """
    tool = TavilySearch(max_results=5, topic="general")
    response = tool.invoke(query)
    return f"Results for '{query}': '{response}'"

def wikipedia_search(query):
    try:
        summary = wikipedia.summary(query, sentences=2)
        return summary
    except Exception as e:
        return f"Error: {e}"

def text_summarizer(text):
    """
    Summarizes the input text using a pre-trained model.
    """
    try:
        summary = summarizer(text, max_length=100, min_length=25, do_sample=False)
        return summary[0]['summary_text']
    except Exception as e:
        return f"Error: {e}"
    
def generate_quiz_with_difficulty(file, difficulty):
    """
    Generates quiz questions and answers from the uploaded file with a specified difficulty level.
    """
    try:
        text = extract_text_from_file(file)
        prompt = f"""
        You are a quiz generator. Based on the following text, create 3 quiz questions and answers.
        The difficulty level should be '{difficulty}'. 
        Text: {text}
        Format the output as:
        Q1: <question>
        A1: <answer>
        Q2: <question>
        A2: <answer>
        Q3: <question>
        A3: <answer>
        """
        response = llm.complete(prompt)
        return response.choices[0].text.strip()
    except Exception as e:
        return f"Error: {e}"
    
    from PyPDF2 import PdfReader

def extract_text_from_file(file):
    """
    Extracts text from a PDF or text file.

    Args:
        file: The uploaded file object.

    Returns:
        str: Extracted text from the file.
    """
    try:
        # Check if the file is a PDF
        if file.name.endswith(".pdf"):
            reader = PdfReader(file)
            text = ""
            for page in reader.pages:
                text += page.extract_text()
            return text
        # Check if the file is a text file
        elif file.name.endswith(".txt"):
            return file.read().decode("utf-8")
        else:
            return "Unsupported file format. Please upload a PDF or text file."
    except Exception as e:
        return f"Error extracting text: {e}"

def essay_validator(essay):
    """
    Validates an essay based on grammar, spelling, and word count.
    """
    try:
        # Check word count
        word_count = len(essay.split())
        if word_count < 100:
            return "Essay is too short. Minimum word count is 100."
        elif word_count > 1000:
            return "Essay is too long. Maximum word count is 1000."

        # Check grammar and spelling using TextBlob
        blob = TextBlob(essay)
        corrected_essay = blob.correct()
        grammar_errors = len(blob.sentences) - len(corrected_essay.sentences)

        # Return validation results
        return f"Word Count: {word_count}\nGrammar Errors: {grammar_errors}\nCorrected Essay:\n{corrected_essay}"
    except Exception as e:
        return f"Error validating essay: {e}"
    
custom_css = """
/* Color for active tab */
.gr-tabitem.selected {
    background: #1976d2 !important;
    color: #fff !important;
}
/* Color for inactive tabs */
.gr-tabitem {
    background: #f0f0f0 !important;
    color: #222 !important;
}
"""

with gr.Blocks(title="MCP server", css=custom_css) as demo:
    gr.Markdown(
        """
        # Educational MCP Server

        Welcome to the Educational MCP Server!  
        This platform provides a suite of AI-powered tools to support your learning and research:

        - **Web Search**: Search the web for up-to-date information using TavilySearch.
        - **Wikipedia Search**: Quickly retrieve concise summaries from Wikipedia.
        - **Text Summarizer**: Summarize long texts into shorter, easy-to-read versions.
        - **Quiz Generator**: Upload a PDF or text file and generate quiz questions at your chosen difficulty.
        - **Essay Validator**: Check your essay for grammar, spelling, and word count.

        Select a tab below to get started!
        """
    )
    gr.Markdown("# MCP server")
    with gr.Tabs():
        with gr.TabItem("Web Search"):
            gr.Markdown("### Web Search")
            search_input = gr.Textbox(label="Search Query")
            search_output = gr.Textbox(label="Results")
            search_btn = gr.Button("Search")
            search_btn.click(
                web_search,
                inputs=search_input,
                outputs=search_output
            )
        with gr.TabItem("Wikipedia Search"):
            gr.Markdown("### Wikipedia Search")
            wiki_input = gr.Textbox(label="Search Wikipedia")
            wiki_output = gr.Textbox(label="Result")
            wiki_btn = gr.Button("Search")
            wiki_btn.click(
                wikipedia_search,
                inputs=wiki_input,
                outputs=wiki_output
            )
        with gr.TabItem("Text Summarizer"):
            gr.Markdown("### Text Summarizer")
            sum_input = gr.Textbox(label="Enter text to summarize")
            sum_output = gr.Textbox(label="Summary")
            sum_btn = gr.Button("Summarize")
            sum_btn.click(
                text_summarizer,
                inputs=sum_input,
                outputs=sum_output
            )
        with gr.TabItem("Quiz Generator"):
            gr.Markdown("### Quiz Generator")
            file_input = gr.File(label="Upload a PDF or Text File")
            difficulty_input = gr.Dropdown(
            label="Select Difficulty Level",
            choices=["Easy", "Medium", "Hard"],
            value="Easy"
            )
            quiz_output = gr.Textbox(label="Quiz Questions and Answers", lines=10)
            quiz_btn = gr.Button("Generate Quiz")
            quiz_btn.click(
               generate_quiz_with_difficulty,
               inputs=[file_input, difficulty_input],
               outputs=quiz_output
            )  
        with gr.TabItem("Essay Validator"):
            gr.Markdown("### Essay Validator")
            essay_input = gr.Textbox(label="Enter your essay", lines=10, placeholder="Paste your essay here...")
            essay_output = gr.Textbox(label="Validation Results", lines=10)
            essay_btn = gr.Button("Validate Essay")
            essay_btn.click(
                essay_validator,
                inputs=essay_input,
                outputs=essay_output
            )    
if __name__ == "__main__":
    demo.launch(mcp_server=True)