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from openai import OpenAI
import gradio as gr
import os
import json
import functools
import random
import datetime
api_key = os.environ.get('FEATHERLESS_API_KEY')
client = OpenAI(
base_url="https://api.featherless.ai/v1",
api_key=api_key
)
def respond(message, history, model):
history_openai_format = []
for human, assistant in history:
history_openai_format.append({"role": "user", "content": human })
history_openai_format.append({"role": "assistant", "content":assistant})
history_openai_format.append({"role": "user", "content": message})
response = client.chat.completions.create(
model=model,
messages= history_openai_format,
temperature=1.0,
stream=True,
max_tokens=2000
)
partial_message = ""
for chunk in response:
if chunk.choices[0].delta.content is not None:
partial_message = partial_message + chunk.choices[0].delta.content
yield partial_message
logo = open('./logo.svg').read()
with open('./model-cache.json', 'r') as f_model_cache:
model_cache = json.load(f_model_cache)
model_class_filter = {
"mistral-v02-7b-std-lc": True,
"llama3-8b-8k": True,
"llama2-solar-10b7-4k": True,
"mistral-nemo-12b-lc": True,
"llama2-13b-4k": True,
"llama3-15b-8k": True,
"qwen2-32b-lc":False,
"llama3-70b-8k":False,
"qwen2-72b-lc":False,
"mixtral-8x22b-lc":False,
"llama3-405b-lc":False,
}
def build_model_choices():
all_choices = []
for model_class in model_cache:
if model_class not in model_class_filter:
print(f"Warning: new model class {model_class}. Treating as blacklisted")
continue
if not model_class_filter[model_class]:
continue
all_choices += [ (f"{model_id} ({model_class})", model_id) for model_id in model_cache[model_class] ]
return all_choices
model_choices = build_model_choices()
def initial_model(referer=None):
if referer == 'http://127.0.0.1:7860/':
return 'Sao10K/Venomia-1.1-m7'
if referer and referer.startswith("https://huggingface.co/"):
possible_model = referer[23:]
full_model_list = functools.reduce(lambda x,y: x+y, model_cache.values(), [])
model_is_supported = possible_model in full_model_list
if model_is_supported:
return possible_model
# let's use a random but different model each day.
key=os.environ.get('RANDOM_SEED', 'kcOtfNHA+e')
o = random.Random(f"{key}-{datetime.date.today().strftime('%Y-%m-%d')}")
return o.choice(model_choices)[1]
title_text="HuggingFace's missing inference widget"
with gr.Blocks(title_text, css='.logo-mark { fill: #ffe184; }') as demo:
gr.HTML("""
<h1 align="center">HuggingFace's missing inference widget</h1>
<p align="center">
Test any <=15B LLM from the hub.
</p>
<h2 align="center">
Please select your model from the list 👇 as HF spaces can't see the refering model card.
</h2>
""")
# hidden_state = gr.State(value=initial_model)
model_selector = gr.Dropdown(
label="Select your Model",
choices=build_model_choices(),
value=initial_model
# value=hidden_state
)
gr.ChatInterface(
respond,
additional_inputs=[model_selector],
head=""",
<script>console.log("Hello from gradio!")</script>
""",
)
gr.HTML(f"""
<p align="center">
Inference by <a href="https://featherless.ai">{logo}</a>
</p>
""")
def update_initial_model_choice(request: gr.Request):
return initial_model(request.headers.get('referer'))
demo.load(update_initial_model_choice, outputs=model_selector)
demo.launch()
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