app.py

import gradio as gr
from huggingface_hub import InferenceClient

client = InferenceClient()

# Gemma 2
model_gemma_2 = "google/gemma-2-27b-it"

def fn_gemma_2(
    prompt,
    history,
    #system_prompt,
    max_tokens,
    temperature,
    top_p,
):
    
    # Without System Prompt
    messages = [{"role": "user", "content": prompt}]
    history.append(messages[0])

    # With System Prompt
    #messages = [{"role": "system", "content": system_prompt}]
    #history.append(messages[0])
    #messages.append({"role": "user", "content": prompt})
    #history.append(messages[1])
    
    stream = client.chat.completions.create(
        model = model_gemma_2,
        messages = history,
        max_tokens = max_tokens,
        temperature = temperature,
        top_p = top_p,
        stream = True,
    )
    
    chunks = []
    for chunk in stream:
        chunks.append(chunk.choices[0].delta.content or "")
        yield "".join(chunks)

app_gemma_2 = gr.ChatInterface(
    fn = fn_gemma_2,
    type = "messages",
    additional_inputs = [
        #gr.Textbox(value="You are a helpful assistant.", label="System Prompt"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max Tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-P"),
    ],
    title = "Google Gemma 2",
    description = model_gemma_2,
)

# Gemma 3
model_gemma_3 = "google/gemma-3-27b-it"

def fn_gemma_3(
    prompt,
    history,
    input,
    #system_prompt,
    max_tokens,
    temperature,
    top_p,
):

    # Without System Prompt
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": prompt
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": input
                    }
                }
            ]
        }
    ]
    history.append(messages[0])

    # With System Prompt
    #messages = [{"role": "system", "content": system_prompt}]
    #history.append(messages[0])
    #messages.append([
    #    {
    #        "role": "user",
    #        "content": [
    #            {
    #                "type": "text",
    #                "text": prompt
    #            },
    #            {
    #                "type": "image_url",
    #                "image_url": {
    #                    "url": input
    #                }
    #            }
    #        ]
    #    }
    #])
    #history.append(messages[1])
    
    stream = client.chat.completions.create(
        model = model_gemma_3,
        messages = history,
        max_tokens = max_tokens,
        temperature = temperature,
        top_p = top_p,
        stream = True,
    )
    
    chunks = []
    for chunk in stream:
        chunks.append(chunk.choices[0].delta.content or "")
        yield "".join(chunks)

app_gemma_3 = gr.ChatInterface(
    fn = fn_gemma_3,
    type = "messages",
    additional_inputs = [
        gr.Textbox(label="Input"),
        #gr.Textbox(value="You are a helpful assistant.", label="System Prompt"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max Tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-P"),
    ],
    title = "Google Gemma 3",
    description = model_gemma_3,
)

app = gr.TabbedInterface(
    [app_gemma_2, app_gemma_3],
    ["Gemma 2", "Gemma 3"]
).launch()