app.py

import gradio as gr
from transformers import pipeline

# Model options dict
MODEL_OPTIONS = {
    "Phi-4 Mini Instruct (recommended)": "microsoft/Phi-4-mini-instruct",
    "Phi-3 Mini Instruct": "microsoft/Phi-3-mini-instruct"
}

model_pipelines = {}

PROMPT_SUGGESTIONS = [
    "Explain quantum computing in simple terms.",
    "Write a friendly email asking for feedback.",
    "Give me a creative short story idea.",
    "Summarize the benefits of meditation."
]

def get_model(model_key):
    if model_key not in model_pipelines:
        model_pipelines[model_key] = pipeline(
            "text-generation",
            model=MODEL_OPTIONS[model_key]
        )
    return model_pipelines[model_key]

def chat_fn(message, history, system_prompt, temperature, chosen_model):
    prompt = f"SYSTEM: {system_prompt}\n"
    for u, a in history:
        prompt += f"User: {u}\nAI: {a}\n"
    prompt += f"User: {message}\nAI:"

    generator = get_model(chosen_model)
    out = generator(
        prompt,
        max_new_tokens=150,
        do_sample=True,
        temperature=float(temperature)
    )[0]["generated_text"]

    reply = out.split("AI:")[-1].strip()
    history.append((message, reply))
    return history, history

with gr.Blocks() as demo:
    gr.Markdown("# Microsoft’s Phi Playground Lab Chat Interface")

    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("**Prompt Suggestions**")
            for s in PROMPT_SUGGESTIONS:
                btn = gr.Button(s)
                # use js= to run client-side JS that updates the textbox
                btn.click(
                    None, 
                    [], 
                    [], 
                    js=f"() => document.querySelector('textarea').value = `{s}`"
                )

        with gr.Column(scale=3):
            sys_prompt = gr.Textbox(
                label="System Prompt",
                value="You are a helpful, concise AI assistant optimized for clarity."
            )
            model_selector = gr.Dropdown(
                choices=list(MODEL_OPTIONS.keys()),
                value="Phi-4 Mini Instruct (recommended)",
                label="Select Model"
            )
            temp_slider = gr.Slider(
                minimum=0.1,
                maximum=1.2,
                step=0.1,
                value=0.7,
                label="Temperature"
            )

            chat_interface = gr.ChatInterface(
                fn=chat_fn,
                additional_inputs=[sys_prompt, temp_slider, model_selector],
                placeholder="Ask anything..."
            )

demo.launch()