app.py

import gradio as gr
from huggingface_hub import InferenceClient

# Initialize Hugging Face Inference Client
client = InferenceClient()

# Function to use a model for generating content dynamically
def generate_content(selected_path):
    """
    Generates dynamic content based on the selected learning path using an NLP model.
    """
    prompt = f"Create a short lesson or problem for the topic: {selected_path}."
    response = client.text_generation(
        model="gpt2",  # Replace with a more suitable Hugging Face model for educational tasks
        inputs=prompt,
        max_new_tokens=150,
        temperature=0.7
    )
    return response["generated_text"]

# Function to evaluate user input (for example, math problem answers)
def evaluate_answer(user_input, selected_path):
    """
    Evaluates user input using the model and provides feedback.
    """
    prompt = f"Evaluate the following response for {selected_path}:\n\nUser Answer: {user_input}\nProvide feedback."
    response = client.text_generation(
        model="gpt2",  # Replace with a more appropriate evaluation model
        inputs=prompt,
        max_new_tokens=100,
        temperature=0.5
    )
    return response["generated_text"]

# Gradio interface
with gr.Blocks() as app:
    gr.Markdown("## AI-Powered Learning Platform")
    gr.Markdown("Choose a topic to start learning:")
    
    # Progress bar at the top (HTML-based)
    gr.HTML('<div style="width: 100%; background-color: lightgray; height: 5px; border-radius: 5px;">'
            '<div style="width: 25%; background-color: green; height: 100%;"></div></div>')
    
    # Options for learning paths
    selected_topic = gr.Radio(
        choices=["Math", "Science & Engineering", "Computer Science & Programming", "Data Science & Data Analysis"],
        label="Select a Learning Path",
        value="Math"
    )
    
    # Buttons for user interaction
    with gr.Row():
        generate_button = gr.Button("Generate Content")
        submit_answer_button = gr.Button("Submit Answer")

    # Outputs
    content_output = gr.Textbox(lines=8, interactive=False, label="Generated Content")
    answer_input = gr.Textbox(lines=2, label="Your Answer")
    feedback_output = gr.Textbox(interactive=False, label="Feedback")

    # Link interactions to actions
    generate_button.click(fn=generate_content, inputs=selected_topic, outputs=content_output)
    submit_answer_button.click(fn=evaluate_answer, inputs=[answer_input, selected_topic], outputs=feedback_output)

# Launch the app
app.launch()