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"""
# 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() |