app.py

import gradio as gr
import requests
import os

# ------------------ GROQ SETTINGS ------------------
GROQ_API_KEY = os.getenv("GROQ_API_KEY") 
GROQ_MODEL = "llama3-70b-8192"

def query_gpt(prompt):
    headers = {
        "Authorization": f"Bearer {GROQ_API_KEY}",
        "Content-Type": "application/json"
    }
    data = {
        "model": GROQ_MODEL,
        "messages": [{"role": "user", "content": prompt}]
    }
    response = requests.post("https://api.groq.com/openai/v1/chat/completions", json=data, headers=headers)
    return response.json()['choices'][0]['message']['content']

# ------------------ SYLLABUS DATA ------------------
class_subject_chapters = {
    "9": {
        "Physics": ["Introduction to Physics", "Kinematics", "Dynamics", "Turning Effect of Forces", "Gravitation", "Work and Energy", "Properties of Matter", "Thermal Properties of Matter", "Transfer of Heat"],
        "Biology": ["Introduction to Biology", "Solving a Biological Problem", "Biodiversity", "Cells and Tissues", "Cell Cycle", "Enzymes", "Bioenergetics", "Nutrition", "Transport"],
        "Chemistry": ["Fundamentals of Chemistry", "Structure of Atoms", "Periodic Table", "Structure of Molecules", "Physical States of Matter", "Solutions", "Electrochemistry", "Chemical Reactivity"]
    },
    "10": {
        "Physics": ["Simple Harmonic Motion and Waves", "Sound", "Geometrical Optics", "Electrostatics", "Current Electricity", "Electromagnetism", "Basic Electronics", "Information and Communication Technology", "Atomic and Nuclear Physics"],
        "Biology": ["Gaseous Exchange", "Homeostasis", "Coordination and Control", "Support and Movement", "Reproduction", "Inheritance", "Man and His Environment", "Biotechnology", "Pharmacology"],
        "Chemistry": ["Chemical Equilibrium", "Acids, Bases and Salts", "Organic Chemistry", "Hydrocarbons", "Biochemistry", "Environmental Chemistry", "Chemical Industries"]
    },
    "11": {
        "Physics": ["Measurements", "Vectors and Equilibrium", "Motion and Force", "Work and Energy", "Circular Motion", "Fluid Dynamics", "Oscillations", "Waves", "Physical Optics", "Thermodynamics"],
        "Biology": ["Introduction to Biology", "Biological Molecules", "Enzymes", "The Cell", "Cell Cycle", "Variety of Life", "Kingdom Prokaryotae", "Kingdom Protista", "Kingdom Fungi", "Kingdom Plantae", "Kingdom Animalia"],
        "Chemistry": ["Basic Concepts", "Experimental Techniques", "Gases", "Liquids and Solids", "Atomic Structure", "Chemical Bonding", "Thermochemistry", "Chemical Equilibrium", "Ionic Equilibrium", "Redox Reactions"]
    },
    "12": {
        "Physics": ["Electrostatics", "Current Electricity", "Electromagnetism", "Electromagnetic Induction", "Alternating Current", "Physics of Solids", "Electronics", "Dawn of Modern Physics", "Atomic Spectra", "Nuclear Physics"],
        "Biology": ["Homeostasis", "Support and Movement", "Coordination and Control", "Reproduction", "Growth and Development", "Chromosomes and DNA", "Biotechnology", "Evolution", "Ecosystem", "Some Major Ecosystems"],
        "Chemistry": ["Periodic Classification", "Transition Elements", "Chemical Reactivity", "Alcohols and Phenols", "Aldehydes and Ketones", "Carboxylic Acids", "Amines", "Polymers", "Biochemistry", "Environmental Chemistry"]
    }
}

# ------------------ FILE READER ------------------
def read_uploaded_file(file):
    if file is None:
        return ""
    if file.name.endswith(".txt"):
        return file.read().decode("utf-8")
    elif file.name.endswith(".pdf"):
        from PyPDF2 import PdfReader
        reader = PdfReader(file)
        return "\n".join(page.extract_text() for page in reader.pages if page.extract_text())
    elif file.name.endswith(".docx"):
        import docx
        doc = docx.Document(file)
        return "\n".join(p.text for p in doc.paragraphs)
    else:
        return "Unsupported file format. Please upload a PDF, DOCX, or TXT file."

# ------------------ MAIN BOT FUNCTION ------------------
def tutor_bot(mode, selected_class, selected_subject, selected_chapter, action_type, uploaded_file):
    if mode == "File Upload Mode" and uploaded_file is not None:
        file_text = read_uploaded_file(uploaded_file)
        if not file_text.strip():
            return "Could not read text from the uploaded file."
        prompt = f"Based on the following content, {action_type.lower()}: \n\n{file_text[:4000]}"  # keep it under token limits
        gpt_response = query_gpt(prompt)
        return gpt_response

    elif mode == "Syllabus Mode":
        if not selected_class or not selected_subject or not selected_chapter:
            return "Please select class, subject, and chapter."
        prompt = f"The user is studying in Class {selected_class}. Give a detailed response for the chapter '{selected_chapter}' in the subject '{selected_subject}'."
        if action_type == "Summarize Important Points":
            prompt = f"Summarize the key points of the chapter '{selected_chapter}' from Class {selected_class} {selected_subject}."
        elif action_type == "Generate MCQs":
            prompt = f"Create 5 multiple choice questions (MCQs) with 4 options each for the chapter '{selected_chapter}' from Class {selected_class} {selected_subject}."
        elif action_type == "Simplify Concepts":
            prompt = f"Explain in very simple terms the important concepts from the chapter '{selected_chapter}' from Class {selected_class} {selected_subject} for a 14-year-old student."

        gpt_response = query_gpt(prompt)
        search_query = f"{selected_class} {selected_subject} {selected_chapter} in Urdu"
        youtube_link = f"https://www.youtube.com/results?search_query={search_query.replace(' ', '+')}"
        gpt_response += f"\n\n🎥 **Watch this topic on YouTube:** [Click here]({youtube_link})"
        return gpt_response

    return "Invalid mode or missing input."

# ------------------ HANGMAN GAME ------------------
def generate_question_answer(class_level, subject, chapter):
    prompt = f"Give one short quiz question and a one-word answer from the chapter '{chapter}' in Class {class_level} {subject}. Format:\nQuestion: ...\nAnswer: ..."
    result = query_gpt(prompt)
    lines = result.strip().split("\n")
    question = ""
    answer = ""
    for line in lines:
        if line.lower().startswith("question:"):
            question = line.split(":", 1)[1].strip()
        elif line.lower().startswith("answer:"):
            answer = line.split(":", 1)[1].strip().lower()
    answer = ''.join(filter(str.isalpha, answer))
    return question, answer

def start_hangman_game(class_level, subject, chapter):
    question, answer = generate_question_answer(class_level, subject, chapter)
    return question, "_" * len(answer), "", 6, answer, []

def guess_letter(letter, answer, current_display, wrong_guesses_left, guessed_letters):
    letter = letter.lower()
    if letter in guessed_letters or len(letter) != 1:
        return current_display, f"You already guessed '{letter}' or invalid guess.", wrong_guesses_left, guessed_letters
    guessed_letters.append(letter)
    if letter in answer:
        new_display = "".join([letter if answer[i] == letter else current_display[i] for i in range(len(answer))])
        if "_" not in new_display:
            return new_display, "🎉 You won!", wrong_guesses_left, guessed_letters
        return new_display, f"✅ Correct guess!", wrong_guesses_left, guessed_letters
    else:
        wrong_guesses_left -= 1
        if wrong_guesses_left == 0:
            return current_display, f"❌ Game over! The correct word was '{answer}'.", wrong_guesses_left, guessed_letters
        return current_display, f"❌ Wrong guess! {wrong_guesses_left} attempts left.", wrong_guesses_left, guessed_letters

# ------------------ GRADIO UI ------------------
with gr.Blocks(title="AI Textbook Tutor") as app:
    with gr.Tab("📘 Tutor"):
        gr.Markdown("## 📘 AI Textbook Tutor\nChoose a mode to either upload your file or select chapter-wise content to get summaries, MCQs, or simplified explanations.")

        mode = gr.Radio(["Syllabus Mode", "File Upload Mode"], label="Select Mode", value="Syllabus Mode")

        with gr.Row():
            class_dropdown = gr.Dropdown(label="Class", choices=list(class_subject_chapters.keys()))
            subject_dropdown = gr.Dropdown(label="Subject")
            chapter_dropdown = gr.Dropdown(label="Chapter")

        file_input = gr.File(label="Upload File (PDF, DOCX, TXT)", file_types=[".pdf", ".docx", ".txt"])
        action_dropdown = gr.Radio(["Summarize Important Points", "Generate MCQs", "Simplify Concepts"], label="Task", value="Summarize Important Points")

        run_button = gr.Button("Run Tutor 🧠")
        output_box = gr.Markdown()

        def update_subjects(selected_class):
            return gr.update(choices=list(class_subject_chapters[selected_class].keys()), value=None)

        def update_chapters(selected_class, selected_subject):
            try:
                chapters = class_subject_chapters[selected_class][selected_subject]
                return gr.update(choices=chapters, value=None)
            except Exception as e:
                return gr.update(choices=[], value="Error")


        def toggle_ui(selected_mode):
            if selected_mode == "File Upload Mode":
                return (
                    gr.update(visible=False),  # Class
                    gr.update(visible=False),  # Subject
                    gr.update(visible=False),  # Chapter
                    gr.update(visible=True)    # File input
                )
            else:
                return (
                    gr.update(visible=True),
                    gr.update(visible=True),
                    gr.update(visible=True),
                    gr.update(visible=False)
                )

        class_dropdown.change(update_subjects, inputs=class_dropdown, outputs=subject_dropdown)
        subject_dropdown.change(update_chapters, inputs=[class_dropdown, subject_dropdown], outputs=chapter_dropdown)
        mode.change(toggle_ui, inputs=mode, outputs=[class_dropdown, subject_dropdown, chapter_dropdown, file_input])

        run_button.click(
            tutor_bot,
            inputs=[mode, class_dropdown, subject_dropdown, chapter_dropdown, action_dropdown, file_input],
            outputs=output_box
        )

    with gr.Tab("🎯 Hangman Game"):
        with gr.Row():
            class_level_hg = gr.Dropdown(label="Class", choices=list(class_subject_chapters.keys()))
            subject_hg = gr.Textbox(label="Subject")
            chapter_hg = gr.Textbox(label="Chapter")
            start_btn = gr.Button("Start Hangman Game")

        question_display = gr.Textbox(label="Question", interactive=False)
        word_display = gr.Textbox(label="Word", interactive=False)
        feedback_display = gr.Textbox(label="Feedback", interactive=False)
        guess_input = gr.Textbox(label="Guess a letter")
        guess_btn = gr.Button("Guess")

        wrong_guesses_left = gr.State(6)
        correct_answer = gr.State("")
        guessed_letters = gr.State([])

        start_btn.click(fn=start_hangman_game, inputs=[class_level_hg, subject_hg, chapter_hg], outputs=[question_display, word_display, feedback_display, wrong_guesses_left, correct_answer, guessed_letters])
        guess_btn.click(fn=guess_letter, inputs=[guess_input, correct_answer, word_display, wrong_guesses_left, guessed_letters], outputs=[word_display, feedback_display, wrong_guesses_left, guessed_letters])

app.launch()