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import streamlit as st
from huggingface_hub import InferenceClient, AsyncInferenceClient
from PIL import Image
from pathlib import Path
import os, subprocess
import requests
st.set_page_config(page_title='HF Inference Client Demo',layout="wide")
# Cache the header of the app to prevent re-rendering on each load
@st.cache_resource
def display_app_header():
"""Display the header of the Streamlit app."""
st.title("HF Inference Client Demo")
st.subheader("Just a little demonstrator")
# Display the header of the app
display_app_header()
# UI sidebar parameters ####################################
st.sidebar.header("Loging")
if hg_token :=st.sidebar.text_input('Enter your HF token', type="password"):
st.sidebar.info('Logged', icon="ℹ️")
else:
st.sidebar.warning("enter your token")
st.sidebar.header("Model")
selected_model = st.sidebar.radio(
"Choose a model or let the client do it",
["Not choose", "Choose"]
)
if selected_model == "Choose":
model = st.sidebar.text_input('Enter a model name. ex : facebook/fastspeech2-en-ljspeech')
else:
model = None
st.sidebar.header("Task")
dict_hg_tasks = {
"Automatic Speech Recognition":"automatic_speech_recognition",
"Text-to-Speech (choose model)":"text_to_speech",
"Image Classification":"image_classification",
"Image Segmentation":"image_segmentation",
"Object Detection":"object_detection",
"Text-to-Image":"text_to_image",
"Visual Question Answering":"visual_question_answering",
"Conversational":"conversational",
"Feature Extraction":"feature_extraction",
"Question Answering":"question_answering",
"Summarization":"summarization",
"Text Classification":"text_classification",
"Text Generation":"text_generation",
"Token Classification":"token_classification",
"Translation (choose model)":"translation",
}
dict_hg_tasks_params = {
"automatic_speech_recognition": {
"input": "upload,url",
"output": "text",
"prompt": False,
"context": False
},
"text_to_speech": {
"input": "text",
"output": "audio",
"prompt": False,
"context": False
},
"image_classification": {
"input": "upload,url",
"output": "image,text",
"prompt": False,
"context": False
},
"image_segmentation": {
"input": "upload,url",
"output": "image,text",
"prompt": False,
"context": False
},
"object_detection": {
"input": "upload,url",
"output": "image,text",
"prompt": False,
"context": False
},
"text_to_image": {
"input": "text",
"output": "image",
"prompt": False,
"context": False
},
"visual_question_answering": {
"input": "upload,url",
"output": "image,text",
"prompt": True,
"context": False
},
"image_to_image": {
"input": "upload,url",
"output": "image,text",
"prompt": True,
"context": False
},
"feature_extraction": {
"input": "text",
"output": "text",
"prompt": False,
"context": False
},
"conversational": {
"input": "text",
"output": "text",
"prompt": False,
"context": False
},
"question_answering": {
"input": None,
"output": "text",
"prompt": True,
"context": True
},
"text_classification": {
"input": "text",
"output": "text",
"prompt": False,
"context": False
},
"token_classification": {
"input": "text",
"output": "text",
"prompt": False,
"context": False
},
"text_generation": {
"input": "text",
"output": "text",
"prompt": False,
"context": False
},
"text_classification": {
"input": "text",
"output": "text",
"prompt": False,
"context": False
},
"translation": {
"input": "text",
"output": "text",
"prompt": False,
"context": False
},
"summarization": {
"input": "text",
"output": "text",
"prompt": False,
"context": False
},
}
selected_task = st.sidebar.radio(
"Choose the task you want to do", # see https://huggingface.co/docs/huggingface_hub/guides/inference"
dict_hg_tasks.keys()
)
st.write(f"The current selected task is : {dict_hg_tasks[selected_task]}")
with st.sidebar.expander("tasks documentation"):
st.write("https://huggingface.co/docs/huggingface_hub/package_reference/inference_client")
# functions ########################################
cwd = os.getcwd()
def get_input(upload,url,text):
if upload is not None:
return upload
else:
if url:
print(url)
return url
elif text:
return text
return None # Default return if neither upload nor url is provided
def display_inputs(task):
if dict_hg_tasks_params[task]["input"] == "upload,url":
return st.file_uploader("Choose a file"),st.text_input("or enter a file url"),""
elif dict_hg_tasks_params[task]["input"] == "text":
return None,"",st.text_input("Enter a text")
else:
return None,"",""
def display_prompt(task):
if dict_hg_tasks_params[task]["prompt"] is True:
return st.text_input("Enter a question")
return None
def display_context(task):
if dict_hg_tasks_params[task]["context"] is True:
return st.text_area("Enter a context")
return None
# UI main client ####################################
if selected_task :
response = None
task = dict_hg_tasks[selected_task]
if model:
client = InferenceClient(model=model)
else:
client = InferenceClient()
uploaded_input,url_input,text_input = display_inputs(task)
prompt_input = display_prompt(task)
context_input = display_context(task)
if get_input(uploaded_input,url_input,text_input):
input = get_input(uploaded_input,url_input,text_input)
if prompt_input is not None:
response = getattr(client, task)(input,question=prompt_input)
else:
response = getattr(client, task)(input)
elif prompt_input:
if context_input is not None:
response = getattr(client, task)(question=prompt_input,context=context_input)
else:
response = getattr(client, task)(input,prompt=prompt_input)
if response is not None:
col1,col2 = st.columns(2)
with col1:
if "text" in dict_hg_tasks_params[task]["output"]:
st.write(response)
elif "audio" in dict_hg_tasks_params[task]["output"]:
Path(os.path.join(cwd,"audio.flac")).write_bytes(response)
st.audio(os.path.join(cwd,"audio.flac"))
with col2:
if dict_hg_tasks_params[task]["output"] == "image,text":
if not(isinstance(input, str)):
image = Image.open(input)
else:
image = Image.open(requests.get(input, stream=True).raw)
st.image(image)
elif dict_hg_tasks_params[task]["output"] == "image":
response.save(os.path.join(cwd,"generated_image.png"))
image = Image.open(os.path.join(cwd,"generated_image.png"))
st.image(image) |