app.py

import gradio as gr
from huggingface_hub import InferenceClient
import tempfile

# Initialize the Hugging Face Inference Client
client = InferenceClient()

# Function to generate dynamic lessons, examples, or code
def generate_content(selected_topic, subtopic, complexity, input_text, examples_count, output_type):
    """
    Generate content dynamically based on user input.

    Args:
        selected_topic (str): Topic selected by the user.
        subtopic (str): Specific subtopic for generation.
        complexity (str): User expertise level.
        input_text (str): Additional input context.
        examples_count (int): Number of examples to generate.
        output_type (str): Desired output format.

    Returns:
        str or dict: Generated content in the selected format.
    """
    # Build the prompt dynamically
    prompt = (
        f"Generate {examples_count} {complexity.lower()}-level {selected_topic.lower()} examples, lessons, "
        f"or problems related to {subtopic}. Context: {input_text}" if input_text.strip()
        else f"Generate {examples_count} {complexity.lower()}-level {selected_topic.lower()} lessons "
             f"or problems focused on {subtopic}."
    )
    messages = [{"role": "user", "content": prompt}]

    try:
        # Generate the content using the model
        response = client.chat.completions.create(
            model="Qwen/Qwen2.5-Coder-32B-Instruct",
            messages=messages,
            temperature=0.5,
            max_tokens=1024,
            top_p=0.7
        )
        content = response.choices[0].message.content

        # Adjust the output based on the selected type
        if output_type == "LaTeX":
            return {"content": content, "latex": True}
        elif output_type == "Downloadable":
            temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt")
            with open(temp_file.name, "w") as file:
                file.write(content)
            return {"file": temp_file.name}
        else:
            return content
    except Exception as e:
        return f"Error: {e}"

# Create the Gradio interface
with gr.Blocks() as app:
    # App Title
    gr.Markdown("## 🌟 Advanced STEM and Code Generator with Interactive Features")

    with gr.Row():
        # Input Panel
        with gr.Column():
            selected_topic = gr.Radio(
                choices=["Math", "STEM", "Code Generation"],
                label="Select a Topic",
                value="Math"
            )
            subtopic = gr.Textbox(
                label="Subtopic",
                placeholder="E.g., Algebra, Physics, Sorting Algorithms"
            )
            complexity = gr.Radio(
                choices=["Beginner", "Intermediate", "Advanced"],
                label="Expertise Level",
                value="Beginner"
            )
            input_text = gr.Textbox(
                label="Additional Context",
                placeholder="E.g., 'Explain integration basics' or 'Generate Python code for searching.'",
                lines=3
            )
            examples_count = gr.Slider(
                minimum=1,
                maximum=5,
                step=1,
                label="Number of Examples",
                value=1
            )
            output_type = gr.Radio(
                choices=["Plain Text", "LaTeX", "Downloadable"],
                label="Output Format",
                value="Plain Text"
            )
            generate_button = gr.Button("Generate Content")

        # Output Panel
        with gr.Column():
            gr.Markdown("### 📝 Output")
            output = gr.Textbox(
                label="Generated Output",
                lines=15,
                interactive=False
            )
            download_button = gr.File(label="Download File (if applicable)")

    # Link the generation function to the button
    generate_button.click(
        fn=generate_content,
        inputs=[selected_topic, subtopic, complexity, input_text, examples_count, output_type],
        outputs=[output, download_button]
    )

    # Feedback Mechanism
    feedback_label = gr.Label(value="Was this content helpful?")
    feedback_rating = gr.Radio(
        choices=["Yes", "No"],
        label="Feedback",
        value="Yes"
    )
    feedback_button = gr.Button("Submit Feedback")

    def collect_feedback(feedback):
        return f"Thank you for your feedback: {feedback}"

    feedback_button.click(
        fn=collect_feedback,
        inputs=[feedback_rating],
        outputs=[feedback_label]
    )

# Launch the Gradio app
app.launch()