app.py

import gradio as gr
from groq import Groq
import os
import threading  # Import threading module

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

# Load Text-to-Image Models
model1 = gr.load("models/prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA")
model2 = gr.load("models/Purz/face-projection")

# Stop event for threading (image generation)
stop_event = threading.Event()

# Function to generate tutor output (lesson, question, feedback)
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": f"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."
        }, {
            "role": "user",
            "content": prompt,
        }],
        model="mixtral-8x7b-32768",  # Model for text generation
        max_tokens=1000,
    )
    
    return completion.choices[0].message.content

# Function to generate images based on model selection
def generate_images(text, selected_model):
    stop_event.clear()

    if selected_model == "Model 1 (Turbo Realism)":
        model = model1
    elif selected_model == "Model 2 (Face Projection)":
        model = model2
    else:
        return ["Invalid model selection."] * 3

    results = []
    for i in range(3):
        if stop_event.is_set():
            return ["Image generation stopped by user."] * 3

        modified_text = f"{text} variation {i+1}"
        result = model(modified_text)
        results.append(result)

    return results

# Set up the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# 🎓 Your AI Tutor with Visuals & Images")

    # Section for generating Text-based output (lesson, question, feedback)
    with gr.Row():
        with gr.Column(scale=2):
            # Input fields for subject, difficulty, and student input for textual output
            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_text = gr.Button("Generate Lesson & Question", 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")
    
    # Section for generating Visual output
    with gr.Row():
        with gr.Column(scale=2):
            # Input fields for text and model selection for image generation
            model_selector = gr.Radio(
                ["Model 1 (Turbo Realism)", "Model 2 (Face Projection)"],
                label="Select Image Generation Model",
                value="Model 1 (Turbo Realism)"
            )
            submit_button_visual = gr.Button("Generate Visuals", variant="primary")
        
        with gr.Column(scale=3):
            # Output fields for generated images
            output1 = gr.Image(label="Generated Image 1")
            output2 = gr.Image(label="Generated Image 2")
            output3 = gr.Image(label="Generated Image 3")
    
    gr.Markdown("""
    ### How to Use
    1. **Text Section**: Select a subject and difficulty, type your query, and click 'Generate Lesson & Question' to get your personalized lesson, comprehension question, and feedback.
    2. **Visual Section**: Select the model for image generation, then click 'Generate Visuals' to receive 3 variations of an image based on your topic.
    3. Review the AI-generated content to enhance your learning experience!
    """)
    
    def process_output_text(subject, difficulty, student_input):
        try:
            tutor_output = generate_tutor_output(subject, difficulty, student_input)
            parsed = eval(tutor_output)  # Convert string to dictionary
            return parsed["lesson"], parsed["question"], parsed["feedback"]
        except:
            return "Error parsing output", "No question available", "No feedback available"
    
    def process_output_visual(text, selected_model):
        try:
            images = generate_images(text, selected_model)  # Generate images
            return images[0], images[1], images[2]
        except:
            return None, None, None
    
    # Generate Text-based Output
    submit_button_text.click(
        fn=process_output_text,
        inputs=[subject, difficulty, student_input],
        outputs=[lesson_output, question_output, feedback_output]
    )
    
    # Generate Visual Output
    submit_button_visual.click(
        fn=process_output_visual,
        inputs=[student_input, model_selector],
        outputs=[output1, output2, output3]
    )

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