app.py

"""
# Inference

import gradio as gr

app = gr.load(
    "google/gemma-2-2b-it",
    src = "models",
    inputs = [gr.Textbox(label = "Input")],
    outputs = [gr.Textbox(label = "Output")],
    title = "Google Gemma",
    description = "Inference",
    examples = [
        ["Hello, World."]
    ]
).launch()
"""
"""
# Pipeline

import gradio as gr
from transformers import pipeline

pipe = pipeline(model = "google/gemma-2-2b-it")

def fn(input):
    output = pipe(
        input,
        max_new_tokens = 2048
    )
    return output[0]["generated_text"]#[len(input):]

app = gr.Interface(
    fn = fn,
    inputs = [gr.Textbox(label = "Input")],
    outputs = [gr.Textbox(label = "Output")],
    title = "Google Gemma",
    description = "Pipeline",
    examples = [
        ["Hello, World."]
    ]
).launch()
"""

import gradio as gr
from huggingface_hub import InferenceClient
import os

hf_token = os.getenv("HF_TOKEN")
client = InferenceClient(api_key=hf_token)

def fn(prompt, history=[]):
    messages = []
    
    for user_prompt, bot_response in history:
        messages.append({"role": "user", "content": user_prompt})
        messages.append({"role": "bot", "content": bot_response})
    
    messages.append({"role": "user", "content": prompt})

    stream = client.chat.completions.create(
        model = "google/gemma-2-2b-it", 
        messages = messages, 
        #temperature = 0.5,
        #max_tokens = 2048,
        #top_p = 0.7,
        stream = True
    )

    bot_response = "".join(chunk.choices[0].delta.content for chunk in stream)

    history.append((prompt, bot_response))
    return bot_response, history

app = gr.Interface(
    fn = fn, 
    inputs = [gr.Textbox(label = "Input")],
    outputs = [gr.Textbox(label = "Output")],
    title = "Google Gemma",
    description = "Chatbot",
    examples = [
        ["Hello, World."]
    ]
).launch()