import gradio as gr
from huggingface_hub import InferenceClient

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

# Function to stream content for Math, STEM, and Code Generation
def generate_stream(selected_topic, input_text):
    """
    Generates dynamic lessons, solutions, or code snippets based on the selected topic.

    Args:
        selected_topic (str): The selected subject (e.g., Math, STEM, or Code Generation).
        input_text (str): Additional input for contextual content generation.

    Yields:
        str: Incremental output content.
    """
    # Create a topic-specific prompt
    prompt = (
        f"Generate a {selected_topic.lower()} lesson, problem, or example based on the following input: {input_text}"
        if input_text.strip() else
        f"Generate a beginner-level {selected_topic.lower()} lesson with examples."
    )
    messages = [{"role": "user", "content": prompt}]

    try:
        # Create a stream for generating content
        stream = client.chat.completions.create(
            model="Qwen/Qwen2.5-Coder-32B-Instruct",  # Streaming model
            messages=messages,
            temperature=0.5,
            max_tokens=1024,
            top_p=0.7,
            stream=True
        )

        # Stream the generated content incrementally
        generated_content = ""
        for chunk in stream:
            generated_content += chunk.choices[0].delta.content
            yield generated_content  # Yield content incrementally
    except Exception as e:
        yield f"Error: {e}"  # Display error if any issues occur

# Create the Gradio interface
with gr.Blocks() as app:
    # App Title and Instructions
    gr.Markdown("## 🎓 STEM Learning and Code Generator")
    gr.Markdown(
        "Get dynamic lessons, problem-solving examples, or code snippets for Math, STEM, "
        "or Computer Science. Select a topic and get started!"
    )

    with gr.Row():
        # Input Section
        with gr.Column():
            selected_topic = gr.Radio(
                choices=["Math", "STEM", "Computer Science (Code Generation)"],
                label="Select a Topic",
                value="Math"  # Default selection
            )
            input_text = gr.Textbox(
                lines=2,
                label="Optional Input",
                placeholder="Provide additional context (e.g., 'Explain calculus basics' or 'Generate Python code for sorting')."
            )
            generate_button = gr.Button("Generate Content")

        # Output Section
        with gr.Column():
            gr.Markdown("### Generated Content")
            output_stream = gr.Textbox(
                lines=15,
                label="Output",
                interactive=False
            )

    # Link the generate button to the streaming function
    generate_button.click(
        fn=generate_stream,
        inputs=[selected_topic, input_text],
        outputs=output_stream
    )

# Launch the Gradio app
app.launch()