app.py

import gradio as gr
from groq import Groq
import os
import matplotlib.pyplot as plt
import numpy as np

# Initialize Groq client with your API key
client = Groq(api_key=os.environ["GROQ_API_KEY"])

def generate_tutor_output(subject, difficulty, student_input):
    prompt = f"""
    You are an expert tutor in {subject} at the {difficulty} level. 
    The student has provided the following input: "{student_input}"
    
    Please generate:
    1. A brief, engaging lesson on the topic (2-3 paragraphs)
    2. A thought-provoking question to check understanding
    3. Constructive feedback on the student's input
    
    Format your response as a JSON object with keys: "lesson", "question", "feedback"
    """
    
    completion = client.chat.completions.create(
        messages=[
            {
                "role": "system",
                "content": "You are the world's best AI tutor, renowned for your ability to explain complex concepts in an engaging, clear, and memorable way and giving math examples. Your expertise in {subject} is unparalleled, and you're adept at tailoring your teaching to {difficulty} level students. Your goal is to not just impart knowledge, but to inspire a love for learning and critical thinking.",
            },
            {
                "role": "user",
                "content": prompt,
            }
        ],
        model="mixtral-8x7b-32768",  # Model for text generation
        max_tokens=1000,
    )
    
    return completion.choices[0].message.content

# Function to generate a simple graph (e.g., bar chart)
def generate_graph():
    # Example data
    x = ['A', 'B', 'C', 'D']
    y = [10, 20, 15, 25]
    
    fig, ax = plt.subplots()
    ax.bar(x, y)
    ax.set_title("Example Bar Chart")
    ax.set_xlabel("Categories")
    ax.set_ylabel("Values")
    
    # Save the plot to a file
    plt.tight_layout()
    plt.savefig("/tmp/bar_chart.png")  # Save to temp directory
    plt.close(fig)
    
    return "/tmp/bar_chart.png"  # Return the path to the saved image

# Set up the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# 🎓 Your AI Tutor")
    
    with gr.Row():
        with gr.Column(scale=2):
            # Input fields for subject, difficulty, and student input
            subject = gr.Dropdown(
                ["Math", "Science", "History", "Literature", "Code", "AI"], 
                label="Subject", 
                info="Choose the subject of your lesson"
            )
            difficulty = gr.Radio(
                ["Beginner", "Intermediate", "Advanced"], 
                label="Difficulty Level", 
                info="Select your proficiency level"
            )
            student_input = gr.Textbox(
                placeholder="Type your query here...", 
                label="Your Input", 
                info="Enter the topic you want to learn"
            )
            submit_button = gr.Button("Generate Lesson", variant="primary")
        
        with gr.Column(scale=3):
            # Output fields for lesson, question, and feedback
            lesson_output = gr.Markdown(label="Lesson")
            question_output = gr.Markdown(label="Comprehension Question")
            feedback_output = gr.Markdown(label="Feedback")
            graph_output = gr.Image(label="Generated Graph")
    
    gr.Markdown("""
    ### How to Use
    1. Select a subject from the dropdown.
    2. Choose your difficulty level.
    3. Enter the topic or question you'd like to explore.
    4. Click 'Generate Lesson' to receive a personalized lesson, question, and feedback.
    5. The AI will also generate a simple bar chart as a visual representation.
    6. Review the AI-generated content to enhance your learning.
    7. Feel free to ask follow-up questions or explore new topics!
    """)
    
    def process_output(output):
        try:
            parsed = eval(output)  # Convert string to dictionary
            graph_path = generate_graph()  # Generate graph
            return parsed["lesson"], parsed["question"], parsed["feedback"], graph_path
        except:
            return "Error parsing output", "No question available", "No feedback available", None
    
    submit_button.click(
        fn=lambda s, d, i: process_output(generate_tutor_output(s, d, i)),
        inputs=[subject, difficulty, student_input],
        outputs=[lesson_output, question_output, feedback_output, graph_output]
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)