import gradio as gr
from huggingface_hub import InferenceClient

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

# Function to stream debate arguments as they are generated
def debate_stream(topic, stance):
    prompt = f"Construct a logical argument for the topic: '{topic}' with a stance: '{stance}'. Include both supporting arguments and potential counterarguments."
    
    messages = [
        {"role": "user", "content": prompt}
    ]

    # Create a stream to receive generated content
    stream = client.chat.completions.create(
        model="Qwen/Qwen2.5-72B-Instruct",
        messages=messages,
        temperature=0.7,
        max_tokens=1024,
        top_p=0.8,
        stream=True
    )

    # Stream content as it is generated
    debate_output = ""
    for chunk in stream:
        debate_output += chunk.choices[0].delta.content
        yield debate_output  # Yield incremental content to display immediately

# Create Gradio interface with the modified layout
with gr.Blocks() as app:
    gr.Markdown("## Automated Debate System")
    gr.Markdown("Generate logical arguments and counterarguments for any debate topic using advanced AI reasoning.")

    with gr.Row():
        # First column for input components
        with gr.Column():
            topic_input = gr.Textbox(lines=2, label="Debate Topic", placeholder="Enter your debate topic here", elem_id="full_width")
            stance = gr.Dropdown(
                choices=["For", "Against"],
                label="Stance",
                value="For"
            )
            debate_button = gr.Button("Generate Debate Arguments")

        # Second column for output
        with gr.Column():
            gr.Markdown("### Debate Arguments")  # This acts as the label for the output
            output_markdown = gr.Markdown()

    # Link button to function with inputs and outputs
    debate_button.click(fn=debate_stream, inputs=[topic_input, stance], outputs=output_markdown)

# Run the Gradio app
app.launch(debug=True)